Artificial Intelligence and Machine Learning
on near-term quantum devices"](https://arxiv.org/abs/1710.01022) [2017]
To compare solutions of participants we use a time-to-solution $T$ metric defined as follows.
Let's assume that to find the ground state of a given molecule (e.g. Helium) a participant makes $N$ runs of their implementation (e.g. $N=3$). A run is considered successful if the ground state found in this run is equal to the ground state known for the molecule to given precision $\delta$ (e.g. $\delta=0.1$). Assume that a single run takes $t$ samples (calls to the quantum computer) on average.
Let $s$ be the probability of success of the participant's VQE implementation (i.e. the number of successful runs divided by the total number of runs $N$).
Let $R$ be the number of runs required to find the ground state with given probability $p$ (e.g. $p=0.6$): \begin{equation} R = {\frac{\log(1-p)}{\log(1-s)}}. \end{equation}
The time-to-solution $T$, defined as the total number of samples used throughout the whole optimisation procedure of VQE, is then calculated as: \begin{equation} T = R \times t. \end{equation}
If the probability of success is $s$, then the probability of failing to find the ground state after $R$ runs is $(1-s)^R$. Therefore, the probability of finding the ground state at least once after $R$ runs is $p =1 - (1-s)^R$. Therefore, the number of runs $R$ required to find the ground state at least once with probability $p$ can be found by solving $p=1-(1-s)^R$.
We can also calculate the standard error associated with the calculated time-to-solution $T$.
From the binomial distribution, the uncertainty $\sigma_s$ in the success probability $s$ is: \begin{equation} \sigma_s=\sqrt{\frac{s(1-s)}{N}} \end{equation} where $N$ is the number of runs used to determine $s$.
The uncertainty in the time taken per run $t$ is: \begin{equation} \sigma_t=\frac{\mathrm{std}}{\sqrt{N}} \end{equation} where $\mathrm{std}$ is the standard deviation of the times taken by all $N$ runs.
The uncertainty in total time taken is: \begin{equation} \sigma_T=\sqrt{0.25 \cdot (T(t+\sigma_t, s) - T(t-\sigma_t,s))^2 + (T(t, s + \sigma_s) - T(t,s))^2} \end{equation}
Please follow instructions here.
$ ck benchmark program:rigetti-vqe \
--env.RIGETTI_QUANTUM_DEVICE=<platform> \
--env.VQE_MINIMIZER_METHOD=<minimizer_method> \
--env.VQE_SAMPLE_SIZE=<sample_number> \
--env.VQE_MAX_ITERATIONS=<max_iterations> \
--record --record_repo=local --record_uoa=<email>-<plaform> \
--tags=qck,hackathon-2018_06_15,<email>,<platform>,<minimizer_method> \
--repetitions=<repetitions>
where:
platform
: 8Q-Agave
or QVM
;minimizer_method
: my_melder_nead
or my_cobyla
or my_minimizer
(as defined in optimizers.py installed under e.g. $CK_TOOLS/hackathon-1.0-linux-64/lib/hackathon
);sample_size
: e.g. 100
(or another resolution);max_iterations
: e.g. 80
(or another cut-off point);email
: a valid email address (later to be replaced with a team id e.g. team-01
);repetitions
: how many times to run the experiment with the given parameters: e.g. 3
.The sample experimental data can be downloaded and registered with CK as follows:
$ wget https://www.dropbox.com/s/a1odux4asze9zpd/ck-quantum-hackathon-20180615.zip
$ ck add repo --zip=ck-quantum-hackathon-20180615.zip
repo_uoa = 'ck-quantum-hackathon-20180615'
!ck list $repo_uoa:experiment:* --print_full | sort
ck-quantum-hackathon-20180615:experiment:team-01-qvm-1 ck-quantum-hackathon-20180615:experiment:team-02-qpu-1 ck-quantum-hackathon-20180615:experiment:team-02-qvm-1 ck-quantum-hackathon-20180615:experiment:team-03-qvm-1 ck-quantum-hackathon-20180615:experiment:team-03-qvm-2 ck-quantum-hackathon-20180615:experiment:team-04-qvm-1 ck-quantum-hackathon-20180615:experiment:team-05-qvm-1 ck-quantum-hackathon-20180615:experiment:team-06-qvm-1 ck-quantum-hackathon-20180615:experiment:team-07-qvm-1 ck-quantum-hackathon-20180615:experiment:team-08-qvm-1 ck-quantum-hackathon-20180615:experiment:team-09-qvm-1 ck-quantum-hackathon-20180615:experiment:team-10-qpu-1 ck-quantum-hackathon-20180615:experiment:team-10-qpu-2 ck-quantum-hackathon-20180615:experiment:team-10-qvm-1 ck-quantum-hackathon-20180615:experiment:team-11-qvm-1 ck-quantum-hackathon-20180615:experiment:team-12-qvm-1 ck-quantum-hackathon-20180615:experiment:team-13-qpu-1 ck-quantum-hackathon-20180615:experiment:team-13-qvm-1 ck-quantum-hackathon-20180615:experiment:team-14-qvm-1
NB: Please ignore this section if you are not interested in re-running or modifying this notebook.
import os
import sys
import json
import re
If some of the scientific packages are missing, please install them using:
# pip install jupyter pandas numpy matplotlib
import IPython as ip
import pandas as pd
import numpy as np
import matplotlib as mp
print ('IPython version: %s' % ip.__version__)
print ('Pandas version: %s' % pd.__version__)
print ('NumPy version: %s' % np.__version__)
print ('Matplotlib version: %s' % mp.__version__)
IPython version: 5.3.0 Pandas version: 0.23.0 NumPy version: 1.14.3 Matplotlib version: 2.2.2
from IPython.display import Image, display
def display_in_full(df):
pd.options.display.max_columns = len(df.columns)
pd.options.display.max_rows = len(df.index)
display(df)
import matplotlib.pyplot as plt
from matplotlib import cm
%matplotlib inline
default_colormap = cm.autumn
default_fontsize = 16
default_barwidth = 0.8
default_figwidth = 16
default_figheight = 8
default_figdpi = 200
default_figsize = [default_figwidth, default_figheight]
if mp.__version__[0]=='2': mp.style.use('classic')
mp.rcParams['figure.max_open_warning'] = 200
mp.rcParams['figure.dpi'] = default_figdpi
mp.rcParams['font.size'] = default_fontsize
mp.rcParams['legend.fontsize'] = 'medium'
If CK is not installed, please install it using:
# pip install ck
import ck.kernel as ck
print ('CK version: %s' % ck.__version__)
CK version: 1.9.4.1
# NB: Make sure the quantum hackathon tool is installed. (It should be if you have run any experiments.)
# $ ck install package --tags=ck-quantum,tool,hackathon,v1
r=ck.access({'action':'show', 'module_uoa':'env', 'tags':'tool,hackathon'})
if r['return']>0:
print ("Error: %s" % r['error'])
exit(1)
# Get the path to the first returned environment entry.
tool_hackathon_dir=r['lst'][0]['meta']['env']['CK_ENV_LIB_HACKATHON_LIB']
sys.path.append(tool_hackathon_dir)
from hackathon.utils import *
def get_experimental_results(repo_uoa, tags='qck', module_uoa='experiment'):
r = ck.access({'action':'search', 'repo_uoa':repo_uoa, 'module_uoa':module_uoa, 'tags':tags})
if r['return']>0:
print('Error: %s' % r['error'])
exit(1)
experiments = r['lst']
dfs = []
for experiment in experiments:
data_uoa = experiment['data_uoa']
r = ck.access({'action':'list_points', 'repo_uoa':repo_uoa, 'module_uoa':module_uoa, 'data_uoa':data_uoa})
if r['return']>0:
print('Error: %s' % r['error'])
exit(1)
tags = r['dict']['tags']
skip = False
# Get team name (final data) or email (submission data).
team_tags = [ tag for tag in tags if tag.startswith('team-') ]
email_tags = [ tag for tag in tags if tag.find('@')!=-1 ]
if len(team_tags) > 0:
team = team_tags[0][0:7]
elif len(email_tags) > 0:
team = email_tags[0]
else:
print('[Warning] Cannot determine team name for experiment in: \'%s\'' % r['path'])
team = 'team-default'
if skip:
print('[Warning] Skipping experiment with bad tags:')
print(tags)
continue
# For each point.
for point in r['points']:
point_file_path = os.path.join(r['path'], 'ckp-%s.0001.json' % point)
with open(point_file_path) as point_file:
point_data_raw = json.load(point_file)
characteristics_list = point_data_raw['characteristics_list']
num_repetitions = len(characteristics_list)
data = [
{
# features
'platform': characteristics['run'].get('vqe_input', {}).get('q_device_name', 'unknown').lower(),
# choices
'minimizer_method': characteristics['run'].get('vqe_input', {}).get('minimizer_method', 'n/a'),
'minimizer_options': characteristics['run'].get('vqe_input', {}).get('minimizer_options', {'maxfev':-1}),
'minimizer_src': characteristics['run'].get('vqe_input', {}).get('minimizer_src', ''),
'sample_number': characteristics['run'].get('vqe_input', {}).get('sample_number','n/a'),
# statistical repetition
'repetition_id': repetition_id,
# runtime characteristics
'run': characteristics['run'],
'report': characteristics['run'].get('report', {}),
'vqe_output': characteristics['run'].get('vqe_output', {}),
}
for (repetition_id, characteristics) in zip(range(num_repetitions), characteristics_list)
if len(characteristics['run']) > 0
]
for datum in data:
datum['team'] = team
datum['point'] = point
datum['success'] = datum.get('vqe_output',{}).get('success',False)
datum['nfev'] = np.int64(datum.get('vqe_output',{}).get('nfev',-1))
datum['nit'] = np.int64(datum.get('vqe_output',{}).get('nit',-1))
datum['fun'] = np.float64(datum.get('vqe_output',{}).get('fun',0))
datum['fun_validated'] = np.float64(datum.get('vqe_output',{}).get('fun_validated',0))
datum['fun_exact'] = np.float64(datum.get('vqe_output',{}).get('fun_exact',0))
datum['total_seconds'] = np.float64(datum.get('report',{}).get('total_seconds',0))
datum['total_q_seconds'] = np.float64(datum.get('report',{}).get('total_q_seconds',0))
datum['total_q_shots'] = np.int64(datum.get('report',{}).get('total_q_shots',0))
tmp_max_iterations = list(datum.get('minimizer_options',{'maxfev':-1}).values())
datum['max_iterations'] = tmp_max_iterations[0] if len(tmp_max_iterations)>0 else -1
index = [
'platform', 'team', 'minimizer_method', 'sample_number', 'max_iterations', 'point', 'repetition_id'
]
# Construct a DataFrame.
df = pd.DataFrame(data)
df = df.set_index(index)
# Append to the list of similarly constructed DataFrames.
dfs.append(df)
if dfs:
# Concatenate all thus constructed DataFrames (i.e. stack on top of each other).
result = pd.concat(dfs)
result.sort_index(ascending=True, inplace=True)
else:
# Construct a dummy DataFrame the success status of which can be safely checked.
result = pd.DataFrame(columns=['success'])
return result
# Merge experimental results from the same team with the same parameters
# (minimizer_method, sample_number, max_iterations) and minimizer source.
def merge_experimental_results(df):
dfs = []
df_prev = None
for index, row in df.iterrows():
# Construct a DataFrame.
df_curr = pd.DataFrame(row).T
# Check if this row is similar to the previous row.
if df_prev is not None: # if not the very first row
if df_prev.index.levels[:-2]==df_curr.index.levels[:-2]: # if the indices match for all but the last two levels
if df_prev.index.levels[-2]!=df_curr.index.levels[-2]: # if the experiments are different
if df_prev['minimizer_src'].values==df_curr['minimizer_src'].values: # if the minimizer source is the same
print('[Info] Merging experiment:')
print(df_curr.index.levels)
print('[Info] into:')
print(df_prev.index.levels)
print('[Info] as:')
# df_curr.index = df_prev.index.copy() # TODO: increment repetition_id
df_curr.index = pd.MultiIndex.from_tuples([(x[0],x[1],x[2],x[3],x[4],x[5],x[6]+1) for x in df_prev.index])
print(df_curr.index.levels)
print
else:
print('[Warning] Cannot merge experiments as the minimizer source is different:')
# print('------------------------------------------------------------------------')
print(df_prev.index.levels)
# print(df_prev['minimizer_src'].values[0])
# print
# print('------------------------------------------------------------------------')
print(df_curr.index.levels)
# print(df_curr['minimizer_src'].values[0])
print
# else:
# print('[Info] Keeping experiments separate:')
# print(df_prev.index.levels)
# print(df_curr.index.levels)
# print
# Append to the list of similarly constructed DataFrames.
dfs.append(df_curr)
# Prepare for next iteration.
df_prev = df_curr
# Concatenate all thus constructed DataFrames (i.e. stack on top of each other).
result = pd.concat(dfs)
result.index.names = df.index.names
result.sort_index(ascending=True, inplace=True)
return result
def get_metrics(df, delta=0.1, prob=0.5, which_fun_key='fun_exact', which_time_key='total_q_shots'):
dfs = []
names_no_repetitions = df.index.names[:-1]
for index, group in df.groupby(level=names_no_repetitions):
# Compute metrics.
classical_energy, minimizer_method, minimizer_src, n_succ, T_ave, T_err, t_ave, t_err, s, s_err = \
benchmark_list_of_runs(group['run'], verbose=False, delta=delta, prob=prob,
which_fun_key=which_fun_key, which_time_key=which_time_key)
# Construct a DataFrame from the metrics.
data = {
# Time to solution.
'T_ave' : T_ave,
'T_err' : T_err,
# Time metric (seconds or shots).
't_ave' : t_ave,
't_err' : t_err,
# Tries metric.
's' : s,
's_err' : s_err
}
data.update({ k : v for (k, v) in zip(names_no_repetitions, index) })
data['num_repetitions'] = len(group)
# NB: index must be something.
df_ = pd.DataFrame(data=data, index=[0])
df_ = df_.set_index(names_no_repetitions)
# Append to the list of similarly constructed DataFrames.
dfs.append(df_)
if dfs:
# Concatenate all thus constructed DataFrames (i.e. stack on top of each other).
result = pd.concat(dfs).dropna()
result.sort_index(ascending=True, inplace=True)
return result
def plot(df, platform_set=None, minimizer_method_set=None, sample_number_set=None, max_iterations_set=None,
markersize_divisor=20,
xmin=0.0, xmax=85.01, xstep=5.00,
ymin=-3.0, ymax=-0.49, ystep=0.25,
figsize=(18,9), dpi=200, legend_loc='lower right'):
platform_set = platform_set or df.index.get_level_values(level='platform').unique()
minimizer_method_set = minimizer_method_set or df.index.get_level_values(level='minimizer_method').unique()
sample_number_set = sample_number_set or df.index.get_level_values(level='sample_number').unique()
max_iterations_set = max_iterations_set or df.index.get_level_values(level='max_iterations').unique()
# Options.
minimizer_method_to_color = {
'my_cobyla' : 'orange',
'my_nelder_mead' : 'green',
'my_minimizer' : 'blue'
}
platform_to_marker = {
'8q-agave' : '8', # octagon
'qvm' : 's', # square
'local_qasm_simulator' : 'p' # pentagon
}
last_marker_size = 10
last_marker_color = 'black'
last_marker_success_true = '^'
last_marker_success_false = 'v'
fig = plt.figure(figsize=figsize, dpi=dpi)
ax = fig.gca()
for index, row in df.iterrows():
(platform, team, minimizer_method, sample_number, max_iterations, point, repetition_id) = index
if platform not in platform_set: continue
if sample_number not in sample_number_set: continue
if minimizer_method not in minimizer_method_set: continue
# NB: This uses 'fun', not 'fun_exact' or 'fun_validated'.
energies = [ iteration['energy'] for iteration in row['report']['iterations'] ]
marker=platform_to_marker[platform]
markersize=sample_number/markersize_divisor
color=minimizer_method_to_color.get(minimizer_method, 'red')
markerfacecolor=color
linestyle='-'
ax.plot(range(len(energies)), energies, marker=marker, color=color, linestyle=linestyle,
markerfacecolor=markerfacecolor, markersize=markersize)
# Mark last function evaluation.
last_energy = energies[-1]
last_fev = row['nfev']-1 if minimizer_method=='my_cobyla' or 'my_nelder_mead' else row['nfev']
last_marker = last_marker_success_true if row['success'] else last_marker_success_false
ax.plot(last_fev, last_energy, color=last_marker_color, marker=last_marker, markersize=last_marker_size)
# Horizontal line for the known ground state.
plt.axhline(y=-2.80778395754, color='red', linestyle='--')
# Vertical lines for max_iterations.
for max_iterations in max_iterations_set:
plt.axvline(x=max_iterations, color='black')
# Grid.
plt.grid()
# Title.
title = 'Variational Quantum Eigensolver (VQE)'
ax.set_title(title)
# X axis.
xlabel='Function evaluation'
ax.set_xlabel(xlabel)
ax.set_xlim(xmin, xmax)
ax.set_xticks(np.arange(xmin, xmax, xstep))
# Y axis.
ylabel='Energy'
ax.set_ylabel(ylabel)
ax.set_ylim(ymin, ymax)
ax.set_yticks(np.arange(ymin, ymax, ystep))
# Legend. https://matplotlib.org/users/legend_guide.html
handles = [
mp.lines.Line2D([], [], label='platform="%s",minimizer_method="%s"' % (p,m), color=minimizer_method_to_color.get(m, 'red'),
marker=platform_to_marker[p], markersize=last_marker_size)
for p in sorted(platform_set)
for m in sorted(minimizer_method_set)
]
handles.append(mp.lines.Line2D([],[], label='ground state', color='red', linestyle='--'))
plt.legend(handles=handles, title='platform,minimizer_method', loc=legend_loc)
# Save figure.
# plt.savefig('vqe.energy.png')
def plot_metric(df, metric='total_q_seconds'):
df.columns.name='metric'
# "df.index.names[:-1]" means reduce along 'repetition_id' (statistical variation).
df_mean = df[[metric]].groupby(level=df.index.names[:-1]).mean().unstack('platform')
df_std = df[[metric]].groupby(level=df.index.names[:-1]).std().unstack('platform')
ax = df_mean.plot(kind='bar', yerr=df_std, grid=True, legend=True, rot=45,
fontsize=default_fontsize, figsize=default_figsize, colormap=default_colormap)
df = get_experimental_results(repo_uoa=repo_uoa)
display_in_full(df)
fun | fun_exact | fun_validated | minimizer_options | minimizer_src | nfev | nit | report | run | success | total_q_seconds | total_q_shots | total_seconds | vqe_output | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
platform | team | minimizer_method | sample_number | max_iterations | point | repetition_id | ||||||||||||||
8q-agave | team-02 | my_minimizer_old | 100 | -1 | 3674f6d98ee1bd3d | 0 | -0.588061 | -0.895430 | -9.953018e-01 | {} | def my_minimizer_old( func, x0, my_args=(), my... | 1 | 1 | {u'total_q_shots': 3600, u'total_q_seconds': 2... | {u'vqe_output': {u'fun_validated': -0.99530180... | False | 200.159730 | 3600 | 200.403136 | {u'fun_validated': -0.995301805612, u'nfev': 1... |
1 | -0.352993 | -0.238422 | -9.814864e-01 | {} | def my_minimizer_old( func, x0, my_args=(), my... | 1 | 1 | {u'total_q_shots': 3600, u'total_q_seconds': 7... | {u'vqe_output': {u'fun_validated': -0.98148640... | False | 70.925749 | 3600 | 71.156245 | {u'fun_validated': -0.981486402517, u'nfev': 1... | ||||||
2 | -1.14816 | -0.819667 | -1.223630e+00 | {} | def my_minimizer_old( func, x0, my_args=(), my... | 1 | 1 | {u'total_q_shots': 3600, u'total_q_seconds': 7... | {u'vqe_output': {u'fun_validated': -1.22362982... | False | 74.202772 | 3600 | 74.447351 | {u'fun_validated': -1.22362982927, u'nfev': 1,... | ||||||
my_nelder_mead | 100 | 80 | e9eb662e8940a18f | 0 | -1.14804 | -0.778078 | -9.954136e-01 | {u'maxfev': 80} | def my_nelder_mead( func, x0, my_args=(), my_o... | 81 | 28 | {u'total_q_shots': 32400, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 620.498547 | 32400 | 683.714966 | {u'status': 1, u'success': False, u'final_simp... | ||
1 | -1.29899 | -0.803944 | -8.357069e-01 | {u'maxfev': 80} | def my_nelder_mead( func, x0, my_args=(), my_o... | 83 | 31 | {u'total_q_shots': 33200, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 790.792448 | 33200 | 792.953200 | {u'status': 1, u'success': False, u'final_simp... | ||||||
2 | -1.14108 | -0.695143 | -6.706099e-01 | {u'maxfev': 80} | def my_nelder_mead( func, x0, my_args=(), my_o... | 82 | 30 | {u'total_q_shots': 32800, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 770.338128 | 32800 | 835.875729 | {u'status': 1, u'success': False, u'final_simp... | ||||||
team-07 | my_cobyla | 50 | 80 | 1de5308445c0a7c2 | 0 | -2.45737 | -2.701098 | -2.467927e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 33 | -1 | {u'total_q_shots': 6600, u'total_q_seconds': 1... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 199.524620 | 6600 | 199.683801 | {u'status': 1, u'maxcv': 0.0, u'success': True... | |
1 | -2.17659 | -2.705198 | -2.345058e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 33 | -1 | {u'total_q_shots': 6600, u'total_q_seconds': 1... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 195.104824 | 6600 | 225.498580 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
2 | -2.2433 | -2.568055 | -2.467927e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 28 | -1 | {u'total_q_shots': 5600, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 222.428922 | 5600 | 222.558790 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
150 | 80 | 27bd6b1f8844e4a4 | 0 | -2.19651 | -2.800783 | -2.209237e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 32 | -1 | {u'total_q_shots': 19200, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 253.490723 | 19200 | 253.972848 | {u'status': 1, u'maxcv': 0.0, u'success': True... | |||
1 | -2.20819 | -2.799510 | -2.278495e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 31 | -1 | {u'total_q_shots': 18600, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 236.086777 | 18600 | 327.784404 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
2 | -2.0234 | -2.803115 | -2.235143e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 31 | -1 | {u'total_q_shots': 18600, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 220.161930 | 18600 | 220.468300 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
300 | 80 | 24e4f93212e61359 | 0 | -2.21927 | -2.800118 | -2.191754e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 29 | -1 | {u'total_q_shots': 34800, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 481.250798 | 34800 | 481.904690 | {u'status': 1, u'maxcv': 0.0, u'success': True... | |||
1 | -2.02209 | -2.534057 | -2.205830e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 31 | -1 | {u'total_q_shots': 37200, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 210.809989 | 37200 | 211.434444 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
2 | -2.26135 | -2.806094 | -2.339705e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 29 | -1 | {u'total_q_shots': 34800, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 213.470225 | 34800 | 214.154729 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
my_nelder_mead | 150 | 80 | d4c9eba098e71597 | 0 | -1.10941 | -0.851760 | -9.772567e-01 | {u'maxfev': 80} | def my_nelder_mead( func, x0, my_args=(), my_o... | 80 | 34 | {u'total_q_shots': 48000, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 612.524059 | 48000 | 707.194151 | {u'status': 1, u'success': False, u'final_simp... | ||
1 | -1.26163 | -0.948764 | -1.122363e+00 | {u'maxfev': 80} | def my_nelder_mead( func, x0, my_args=(), my_o... | 80 | 32 | {u'total_q_shots': 48000, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 640.798323 | 48000 | 641.993766 | {u'status': 1, u'success': False, u'final_simp... | ||||||
2 | -1.22883 | -0.839324 | -1.045316e+00 | {u'maxfev': 80} | def my_nelder_mead( func, x0, my_args=(), my_o... | 80 | 29 | {u'total_q_shots': 48000, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 692.888153 | 48000 | 694.106954 | {u'status': 1, u'success': False, u'final_simp... | ||||||
team-10 | my_minimizer | 1 | 8 | 225212636444a960 | 0 | -2.80778 | -2.749625 | -2.807784e+00 | {u'maxfev': 8} | def my_minimizer( func, x0, my_args=(), my_opt... | 8 | 8 | {u'total_q_shots': 32, u'total_q_seconds': 69.... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 69.911630 | 32 | 100.556974 | {u'fun_validated': -2.80778395754, u'nfev': 8,... | |
1 | -3.33564 | -2.572884 | -1.403892e+00 | {u'maxfev': 8} | def my_minimizer( func, x0, my_args=(), my_opt... | 8 | 8 | {u'total_q_shots': 32, u'total_q_seconds': 60.... | {u'vqe_output': {u'fun_validated': -1.40389197... | False | 60.816362 | 32 | 60.825788 | {u'fun_validated': -1.40389197877, u'nfev': 8,... | ||||||
2 | -2.80778 | -2.688101 | -2.807784e+00 | {u'maxfev': 8} | def my_minimizer( func, x0, my_args=(), my_opt... | 8 | 8 | {u'total_q_shots': 32, u'total_q_seconds': 46.... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 46.797893 | 32 | 46.807505 | {u'fun_validated': -2.80778395754, u'nfev': 8,... | ||||||
10 | 8 | a3842f89879d0961 | 0 | -3.01893 | -2.434533 | -2.510155e+00 | {u'maxfev': 8} | def my_minimizer( func, x0, my_args=(), my_opt... | 8 | 8 | {u'total_q_shots': 320, u'total_q_seconds': 49... | {u'vqe_output': {u'fun_validated': -2.51015540... | False | 49.444814 | 320 | 49.458719 | {u'fun_validated': -2.51015540172, u'nfev': 8,... | |||
1 | -2.22938 | -2.714853 | -2.369766e+00 | {u'maxfev': 8} | def my_minimizer( func, x0, my_args=(), my_opt... | 8 | 8 | {u'total_q_shots': 320, u'total_q_seconds': 10... | {u'vqe_output': {u'fun_validated': -2.36976620... | False | 109.043486 | 320 | 109.056744 | {u'fun_validated': -2.36976620384, u'nfev': 8,... | ||||||
2 | -2.51016 | -2.492311 | -1.790242e+00 | {u'maxfev': 8} | def my_minimizer( func, x0, my_args=(), my_opt... | 8 | 8 | {u'total_q_shots': 320, u'total_q_seconds': 51... | {u'vqe_output': {u'fun_validated': -1.79024166... | False | 51.897093 | 320 | 82.222118 | {u'fun_validated': -1.7902416688, u'nfev': 8, ... | ||||||
my_nelder_mead | 1 | 8 | f6a07a94b24cf38d | 0 | -1.93175 | -0.787939 | 1.110223e-16 | {u'maxfev': 8} | def my_nelder_mead( func, x0, my_args=(), my_o... | 11 | 3 | {u'total_q_shots': 44, u'total_q_seconds': 80.... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 80.661713 | 44 | 80.691590 | {u'status': 1, u'success': False, u'final_simp... | ||
1 | -2.80778 | -0.756596 | 1.110223e-16 | {u'maxfev': 8} | def my_nelder_mead( func, x0, my_args=(), my_o... | 11 | 3 | {u'total_q_shots': 44, u'total_q_seconds': 80.... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 80.048970 | 44 | 80.078401 | {u'status': 1, u'success': False, u'final_simp... | ||||||
2 | -2.80778 | -0.787939 | 1.110223e-16 | {u'maxfev': 8} | def my_nelder_mead( func, x0, my_args=(), my_o... | 9 | 3 | {u'total_q_shots': 36, u'total_q_seconds': 70.... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 70.736745 | 36 | 70.758163 | {u'status': 1, u'success': False, u'final_simp... | ||||||
100 | 80 | 4485d80395eb5874 | 0 | -1.19398 | -0.778102 | -1.081444e+00 | {u'maxfev': 80} | def my_nelder_mead( func, x0, my_args=(), my_o... | 81 | 31 | {u'total_q_shots': 32400, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 619.284162 | 32400 | 651.034322 | {u'status': 1, u'success': False, u'final_simp... | |||
1 | -1.2343 | -0.768290 | -9.690207e-01 | {u'maxfev': 80} | def my_nelder_mead( func, x0, my_args=(), my_o... | 80 | 32 | {u'total_q_shots': 32000, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 588.112231 | 32000 | 588.584697 | {u'status': 1, u'success': False, u'final_simp... | ||||||
2 | -1.13064 | -0.819667 | -9.690207e-01 | {u'maxfev': 80} | def my_nelder_mead( func, x0, my_args=(), my_o... | 80 | 31 | {u'total_q_shots': 32000, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 593.561087 | 32000 | 594.024916 | {u'status': 1, u'success': False, u'final_simp... | ||||||
my_random_sampler | 100 | 10 | 251cad05f514492a | 0 | -2.25723 | -2.415852 | -1.944782e+00 | {u'maxfev': 10} | def my_random_sampler( func, x0, my_args=(), m... | 10 | 10 | {u'total_q_shots': 4000, u'total_q_seconds': 6... | {u'vqe_output': {u'fun_validated': -1.94478154... | False | 66.203098 | 4000 | 66.263351 | {u'fun_validated': -1.94478154481, u'nfev': 10... | ||
1 | -2.16244 | -2.731297 | -2.202872e+00 | {u'maxfev': 10} | def my_random_sampler( func, x0, my_args=(), m... | 10 | 10 | {u'total_q_shots': 4000, u'total_q_seconds': 8... | {u'vqe_output': {u'fun_validated': -2.20287242... | False | 80.050180 | 4000 | 80.109609 | {u'fun_validated': -2.20287242405, u'nfev': 10... | ||||||
2 | -2.25566 | -2.628227 | -2.150199e+00 | {u'maxfev': 10} | def my_random_sampler( func, x0, my_args=(), m... | 10 | 10 | {u'total_q_shots': 4000, u'total_q_seconds': 1... | {u'vqe_output': {u'fun_validated': -2.15019853... | False | 139.305192 | 4000 | 139.368401 | {u'fun_validated': -2.15019853526, u'nfev': 10... | ||||||
team-12 | my_minimizer | 1 | 1 | 3174739978ea2d37 | 0 | -1.40389 | -2.068303 | -1.403892e+00 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 70 | 1 | {u'total_q_shots': 284, u'total_q_seconds': 58... | {u'vqe_output': {u'fun_validated': -1.40389197... | False | 586.032463 | 284 | 636.829853 | {u'fun_validated': -1.40389197877, u'nfev': 70... | |
5 | 1 | 827f495dba576e4a | 0 | -2.00138 | -2.699187 | -2.738148e+00 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 200 | 1 | {u'total_q_shots': 820, u'total_q_seconds': 25... | {u'vqe_output': {u'fun_validated': -2.73814815... | False | 251.747914 | 820 | 268.701988 | {u'fun_validated': -2.73814815033, u'nfev': 20... | |||
team-13 | my_grid_sampler | 50 | -1 | 6bc43f399ba11566 | 0 | -2.55553 | -2.807745 | -2.478260e+00 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 9 | 9 | {u'total_q_shots': 2000, u'total_q_seconds': 5... | {u'vqe_output': {u'fun_validated': -2.47826049... | False | 56.147386 | 2000 | 56.218286 | {u'fun_validated': -2.47826049674, u'nfev': 9,... | |
b1c368d747a2b5fb | 0 | -1.93422 | -2.749526 | -2.404584e+00 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 25 | 25 | {u'total_q_shots': 5200, u'total_q_seconds': 1... | {u'vqe_output': {u'fun_validated': -2.40458410... | False | 146.762191 | 5200 | 146.884449 | {u'fun_validated': -2.40458410744, u'nfev': 25... | |||||
100 | -1 | 6f64ff6b83067e89 | 0 | -2.32731 | -2.807762 | -2.269362e+00 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 9 | 9 | {u'total_q_shots': 4000, u'total_q_seconds': 5... | {u'vqe_output': {u'fun_validated': -2.26936171... | False | 57.765576 | 4000 | 57.857605 | {u'fun_validated': -2.26936171593, u'nfev': 9,... | |||
qvm | team-01 | my_cobyla | 50 | 80 | c6e2a72de42fba64 | 0 | -2.80778 | -2.802874 | -2.807784e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 22 | -1 | {u'total_q_shots': 4400, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 20.532208 | 4400 | 20.676123 | {u'status': 1, u'maxcv': 0.0, u'success': True... |
1 | -2.63932 | -2.750296 | -2.751628e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 33 | -1 | {u'total_q_shots': 6600, u'total_q_seconds': 3... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 31.853930 | 6600 | 32.015083 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
2 | -2.75163 | -2.755946 | -2.751628e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 31 | -1 | {u'total_q_shots': 6200, u'total_q_seconds': 3... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 35.015321 | 6200 | 35.164022 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
my_grid_sampler | 10 | -1 | 9dc38b214d7fbb38 | 0 | -2.80778 | -2.807784 | -2.807784e+00 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 10 | 10 | {u'total_q_shots': 4000, u'total_q_seconds': 1... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 107.699277 | 4000 | 107.935334 | {u'fun_validated': -2.80778395754, u'nfev': 10... | ||
1 | -2.80778 | -2.807784 | -2.807784e+00 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 10 | 10 | {u'total_q_shots': 4000, u'total_q_seconds': 9... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 95.906390 | 4000 | 96.099248 | {u'fun_validated': -2.80778395754, u'nfev': 10... | ||||||
2 | -2.80778 | -2.807784 | -2.807784e+00 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 10 | 10 | {u'total_q_shots': 4000, u'total_q_seconds': 1... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 104.597146 | 4000 | 104.784505 | {u'fun_validated': -2.80778395754, u'nfev': 10... | ||||||
team-02 | my_minimizer | 100 | 1 | 0acf6e59aa3474f9 | 0 | -0.715985 | -0.819667 | -7.300238e-01 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 1 | 1 | {u'total_q_shots': 400, u'total_q_seconds': 1.... | {u'vqe_output': {u'fun_validated': -0.73002382... | False | 1.477609 | 400 | 1.508579 | {u'fun_validated': -0.73002382896, u'nfev': 1,... | |
1 | -0.842335 | -0.819667 | -7.300238e-01 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 1 | 1 | {u'total_q_shots': 400, u'total_q_seconds': 2.... | {u'vqe_output': {u'fun_validated': -0.73002382... | False | 2.489254 | 400 | 2.521852 | {u'fun_validated': -0.73002382896, u'nfev': 1,... | ||||||
2 | -0.701946 | -0.819667 | -7.581017e-01 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 1 | 1 | {u'total_q_shots': 400, u'total_q_seconds': 1.... | {u'vqe_output': {u'fun_validated': -0.75810166... | False | 1.144001 | 400 | 1.178340 | {u'fun_validated': -0.758101668536, u'nfev': 1... | ||||||
320b44ecbb1ec4e2 | 0 | -0.744063 | -0.819667 | -7.159849e-01 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 1 | 1 | {u'total_q_shots': 3600, u'total_q_seconds': 1... | {u'vqe_output': {u'fun_validated': -0.71598490... | False | 10.103147 | 3600 | 10.370854 | {u'fun_validated': -0.715984909173, u'nfev': 1... | |||||
4d6b91ed69ffc3ac | 0 | -0.814257 | -0.819667 | -7.440627e-01 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 1 | 1 | {u'total_q_shots': 3600, u'total_q_seconds': 1... | {u'vqe_output': {u'fun_validated': -0.74406274... | False | 10.178863 | 3600 | 10.425309 | {u'fun_validated': -0.744062748748, u'nfev': 1... | |||||
8e654689bc7e50ae | 0 | -0.814257 | -0.819667 | -8.563741e-01 | {u'maxfev': 1} | def my_minimizer(func, x0, my_args=(), my_opti... | 1 | 1 | {u'total_q_shots': 400, u'total_q_seconds': 1.... | {u'vqe_output': {u'fun_validated': -0.85637410... | False | 1.342325 | 400 | 1.368846 | {u'fun_validated': -0.85637410705, u'nfev': 1,... | |||||
d97add24d3da668c | 0 | 1.11022e-16 | -0.699686 | -8.423352e-01 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 1 | 1 | {u'total_q_shots': 3600, u'total_q_seconds': 8... | {u'vqe_output': {u'fun_validated': -0.84233518... | False | 8.724857 | 3600 | 8.999953 | {u'fun_validated': -0.842335187262, u'nfev': 1... | |||||
1 | -0.730024 | -0.819667 | -7.159849e-01 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 1 | 1 | {u'total_q_shots': 3600, u'total_q_seconds': 1... | {u'vqe_output': {u'fun_validated': -0.71598490... | False | 10.767656 | 3600 | 11.035455 | {u'fun_validated': -0.715984909173, u'nfev': 1... | ||||||
2 | -0.280778 | -1.504542 | -1.446009e+00 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 1 | 1 | {u'total_q_shots': 3600, u'total_q_seconds': 9... | {u'vqe_output': {u'fun_validated': -1.44600873... | False | 9.317932 | 3600 | 9.583967 | {u'fun_validated': -1.44600873813, u'nfev': 1,... | ||||||
e9eb662e8940a18f | 3 | -0.744063 | -0.819667 | -7.159849e-01 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 1 | 1 | {u'total_q_shots': 3600, u'total_q_seconds': 1... | {u'vqe_output': {u'fun_validated': -0.71598490... | False | 10.103147 | 3600 | 10.370854 | {u'fun_validated': -0.715984909173, u'nfev': 1... | |||||
my_minimizer_old | 100 | -1 | 4f31e741e5931b12 | 0 | 1.11022e-16 | -1.006137 | -8.282963e-01 | {} | def my_minimizer_old( func, x0, my_args=(), my... | 1 | 1 | {u'total_q_shots': 3600, u'total_q_seconds': 8... | {u'vqe_output': {u'fun_validated': -0.82829626... | False | 8.739747 | 3600 | 9.001060 | {u'fun_validated': -0.828296267474, u'nfev': 1... | ||
1 | -0.112311 | -0.107069 | -7.019460e-02 | {} | def my_minimizer_old( func, x0, my_args=(), my... | 1 | 1 | {u'total_q_shots': 3600, u'total_q_seconds': 9... | {u'vqe_output': {u'fun_validated': -0.07019459... | False | 9.499269 | 3600 | 9.751628 | {u'fun_validated': -0.0701945989385, u'nfev': ... | ||||||
2 | -0.687907 | -0.819667 | -9.827244e-01 | {} | def my_minimizer_old( func, x0, my_args=(), my... | 1 | 1 | {u'total_q_shots': 3600, u'total_q_seconds': 9... | {u'vqe_output': {u'fun_validated': -0.98272438... | False | 9.294229 | 3600 | 9.533971 | {u'fun_validated': -0.982724385139, u'nfev': 1... | ||||||
team-03 | my_cobyla | 2 | 3 | 240e78c79fe4651a | 0 | 1.11022e-16 | -0.819667 | 1.110223e-16 | {u'maxiter': 3} | def my_cobyla( func, x0, my_args=(), my_option... | 3 | -1 | {u'total_q_shots': 24, u'total_q_seconds': 2.7... | {u'vqe_output': {u'status': 2, u'maxcv': 0.0, ... | False | 2.719554 | 24 | 2.729256 | {u'status': 2, u'maxcv': 0.0, u'success': Fals... | |
1 | -0.701946 | -0.819667 | -1.403892e+00 | {u'maxiter': 3} | def my_cobyla( func, x0, my_args=(), my_option... | 3 | -1 | {u'total_q_shots': 24, u'total_q_seconds': 2.4... | {u'vqe_output': {u'status': 2, u'maxcv': 0.0, ... | False | 2.430117 | 24 | 2.445950 | {u'status': 2, u'maxcv': 0.0, u'success': Fals... | ||||||
2 | -0.701946 | -0.819667 | 1.110223e-16 | {u'maxiter': 3} | def my_cobyla( func, x0, my_args=(), my_option... | 3 | -1 | {u'total_q_shots': 24, u'total_q_seconds': 3.1... | {u'vqe_output': {u'status': 2, u'maxcv': 0.0, ... | False | 3.190080 | 24 | 3.205384 | {u'status': 2, u'maxcv': 0.0, u'success': Fals... | ||||||
30 | e704e00811f08d88 | 0 | -2.80778 | -2.573724 | -2.807784e+00 | {u'maxiter': 30} | def my_cobyla( func, x0, my_args=(), my_option... | 15 | -1 | {u'total_q_shots': 120, u'total_q_seconds': 19... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 19.275942 | 120 | 19.325948 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||
1 | -2.10584 | -2.573627 | -2.807784e+00 | {u'maxiter': 30} | def my_cobyla( func, x0, my_args=(), my_option... | 18 | -1 | {u'total_q_shots': 144, u'total_q_seconds': 21... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 21.842086 | 144 | 21.908726 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
2 | -0.701946 | -0.664905 | -7.019460e-01 | {u'maxiter': 30} | def my_cobyla( func, x0, my_args=(), my_option... | 27 | -1 | {u'total_q_shots': 216, u'total_q_seconds': 31... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 31.690584 | 216 | 31.793028 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
5 | 10 | aa64cff4b1d94d38 | 0 | -2.80778 | -2.573724 | -2.807784e+00 | {u'maxiter': 10} | def my_cobyla( func, x0, my_args=(), my_option... | 10 | -1 | {u'total_q_shots': 200, u'total_q_seconds': 8.... | {u'vqe_output': {u'status': 2, u'maxcv': 0.0, ... | False | 8.914804 | 200 | 8.964248 | {u'status': 2, u'maxcv': 0.0, u'success': Fals... | |||
1 | -2.80778 | -2.573724 | -2.527006e+00 | {u'maxiter': 10} | def my_cobyla( func, x0, my_args=(), my_option... | 10 | -1 | {u'total_q_shots': 200, u'total_q_seconds': 8.... | {u'vqe_output': {u'status': 2, u'maxcv': 0.0, ... | False | 8.491021 | 200 | 8.552706 | {u'status': 2, u'maxcv': 0.0, u'success': Fals... | ||||||
2 | -2.80778 | -2.801742 | -2.807784e+00 | {u'maxiter': 10} | def my_cobyla( func, x0, my_args=(), my_option... | 10 | -1 | {u'total_q_shots': 200, u'total_q_seconds': 8.... | {u'vqe_output': {u'status': 2, u'maxcv': 0.0, ... | False | 8.070002 | 200 | 8.103120 | {u'status': 2, u'maxcv': 0.0, u'success': Fals... | ||||||
10 | 10 | 4ffebda70760cefc | 0 | -2.80778 | -2.737779 | -2.807784e+00 | {u'maxiter': 10} | def my_cobyla( func, x0, my_args=(), my_option... | 10 | -1 | {u'total_q_shots': 400, u'total_q_seconds': 8.... | {u'vqe_output': {u'status': 2, u'maxcv': 0.0, ... | False | 8.558165 | 400 | 8.623666 | {u'status': 2, u'maxcv': 0.0, u'success': Fals... | |||
1 | -2.80778 | -2.573724 | -2.527006e+00 | {u'maxiter': 10} | def my_cobyla( func, x0, my_args=(), my_option... | 10 | -1 | {u'total_q_shots': 400, u'total_q_seconds': 9.... | {u'vqe_output': {u'status': 2, u'maxcv': 0.0, ... | False | 9.531450 | 400 | 9.576746 | {u'status': 2, u'maxcv': 0.0, u'success': Fals... | ||||||
2 | -2.80778 | -2.794750 | -2.807784e+00 | {u'maxiter': 10} | def my_cobyla( func, x0, my_args=(), my_option... | 10 | -1 | {u'total_q_shots': 400, u'total_q_seconds': 9.... | {u'vqe_output': {u'status': 2, u'maxcv': 0.0, ... | False | 9.971071 | 400 | 10.014225 | {u'status': 2, u'maxcv': 0.0, u'success': Fals... | ||||||
50 | 1 | c6e3ed24f7842f6a | 0 | -1.0108 | -0.819667 | -9.546465e-01 | {u'maxiter': 1} | def my_cobyla( func, x0, my_args=(), my_option... | 1 | -1 | {u'total_q_shots': 200, u'total_q_seconds': 0.... | {u'vqe_output': {u'status': 2, u'maxcv': 0.0, ... | False | 0.790883 | 200 | 0.806937 | {u'status': 2, u'maxcv': 0.0, u'success': Fals... | |||
1 | -0.758102 | -0.819667 | -8.423352e-01 | {u'maxiter': 1} | def my_cobyla( func, x0, my_args=(), my_option... | 1 | -1 | {u'total_q_shots': 200, u'total_q_seconds': 0.... | {u'vqe_output': {u'status': 2, u'maxcv': 0.0, ... | False | 0.806394 | 200 | 0.817754 | {u'status': 2, u'maxcv': 0.0, u'success': Fals... | ||||||
2 | -0.898491 | -0.819667 | -9.546465e-01 | {u'maxiter': 1} | def my_cobyla( func, x0, my_args=(), my_option... | 1 | -1 | {u'total_q_shots': 200, u'total_q_seconds': 0.... | {u'vqe_output': {u'status': 2, u'maxcv': 0.0, ... | False | 0.838187 | 200 | 0.866396 | {u'status': 2, u'maxcv': 0.0, u'success': Fals... | ||||||
30 | 3c1a71fb284ef7a3 | 0 | -2.80778 | -2.801494 | -2.807784e+00 | {u'maxiter': 30} | def my_cobyla( func, x0, my_args=(), my_option... | 21 | -1 | {u'total_q_shots': 4200, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 26.665660 | 4200 | 26.928278 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||
1 | -2.77971 | -2.803899 | -2.807784e+00 | {u'maxiter': 30} | def my_cobyla( func, x0, my_args=(), my_option... | 24 | -1 | {u'total_q_shots': 4800, u'total_q_seconds': 3... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 33.877902 | 4800 | 34.197363 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
2 | -2.80778 | -2.797289 | -2.807784e+00 | {u'maxiter': 30} | def my_cobyla( func, x0, my_args=(), my_option... | 21 | -1 | {u'total_q_shots': 4200, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 25.411273 | 4200 | 25.705435 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
b1b31421eaffd55a | 0 | -2.80778 | -2.806300 | -2.807784e+00 | {u'maxiter': 30} | def my_cobyla( func, x0, my_args=(), my_option... | 18 | -1 | {u'total_q_shots': 3600, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 21.377928 | 3600 | 21.610705 | {u'status': 1, u'maxcv': 0.0, u'success': True... | |||||
1 | -2.80778 | -2.805353 | -2.807784e+00 | {u'maxiter': 30} | def my_cobyla( func, x0, my_args=(), my_option... | 18 | -1 | {u'total_q_shots': 3600, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 22.572149 | 3600 | 22.717782 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
2 | -2.80778 | -2.800747 | -2.779706e+00 | {u'maxiter': 30} | def my_cobyla( func, x0, my_args=(), my_option... | 20 | -1 | {u'total_q_shots': 4000, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 22.920551 | 4000 | 23.158812 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
cbfaa4139b8b0662 | 0 | -2.80778 | -2.806559 | -2.807784e+00 | {u'maxiter': 30} | def my_cobyla( func, x0, my_args=(), my_option... | 22 | -1 | {u'total_q_shots': 4400, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 26.549824 | 4400 | 26.827078 | {u'status': 1, u'maxcv': 0.0, u'success': True... | |||||
1 | -2.77971 | -2.767745 | -2.751628e+00 | {u'maxiter': 30} | def my_cobyla( func, x0, my_args=(), my_option... | 29 | -1 | {u'total_q_shots': 5800, u'total_q_seconds': 3... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 30.500462 | 5800 | 30.873339 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
2 | -2.80778 | -2.762958 | -2.751628e+00 | {u'maxiter': 30} | def my_cobyla( func, x0, my_args=(), my_option... | 25 | -1 | {u'total_q_shots': 5000, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 26.488012 | 5000 | 26.856112 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
100 | 80 | bb013002e7edb3a3 | 0 | -2.80778 | -2.798999 | -2.807784e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 22 | -1 | {u'total_q_shots': 8800, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 26.205400 | 8800 | 26.418594 | {u'status': 1, u'maxcv': 0.0, u'success': True... | |||
1 | -2.80778 | -2.800626 | -2.807784e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 24 | -1 | {u'total_q_shots': 9600, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 26.868358 | 9600 | 27.146076 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
2 | -2.80778 | -2.780713 | -2.779706e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 31 | -1 | {u'total_q_shots': 12400, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 32.150482 | 12400 | 32.481345 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
my_minimizer | 5 | 10 | 2b74910afe36e6b5 | 0 | -1.68467 | -1.977062 | -1.684670e+00 | {u'maxfev': 10} | def my_minimizer( func, x0, my_args=(), my_opt... | 15 | 1 | {u'total_q_shots': 300, u'total_q_seconds': 16... | {u'vqe_output': {u'status': 0, u'success': Tru... | True | 16.595962 | 300 | 16.677765 | {u'status': 0, u'success': True, u'fun_validat... | ||
1 | -2.80778 | -2.807784 | -2.807784e+00 | {u'maxfev': 10} | def my_minimizer( func, x0, my_args=(), my_opt... | 26 | 4 | {u'total_q_shots': 520, u'total_q_seconds': 27... | {u'vqe_output': {u'status': 0, u'success': Tru... | True | 27.988790 | 520 | 28.113969 | {u'status': 0, u'success': True, u'fun_validat... | ||||||
2 | -1.96545 | -1.977062 | -1.123114e+00 | {u'maxfev': 10} | def my_minimizer( func, x0, my_args=(), my_opt... | 18 | 2 | {u'total_q_shots': 360, u'total_q_seconds': 20... | {u'vqe_output': {u'status': 4, u'success': Fal... | False | 20.714807 | 360 | 20.795539 | {u'status': 4, u'success': False, u'fun_valida... | ||||||
9f9bf704d869c823 | 0 | -2.80778 | -2.807260 | -2.807784e+00 | {u'maxfev': 10} | def my_minimizer( func, x0, my_args=(), my_opt... | 88 | 7 | {u'total_q_shots': 1760, u'total_q_seconds': 8... | {u'vqe_output': {u'status': 0, u'success': Tru... | True | 82.977424 | 1760 | 83.429854 | {u'status': 0, u'success': True, u'fun_validat... | |||||
1 | -0.280778 | -0.063559 | 1.110223e-16 | {u'maxfev': 10} | def my_minimizer( func, x0, my_args=(), my_opt... | 32 | 3 | {u'total_q_shots': 640, u'total_q_seconds': 37... | {u'vqe_output': {u'status': 0, u'success': Tru... | True | 37.521370 | 640 | 37.666703 | {u'status': 0, u'success': True, u'fun_validat... | ||||||
2 | 1.11022e-16 | -0.126606 | 1.110223e-16 | {u'maxfev': 10} | def my_minimizer( func, x0, my_args=(), my_opt... | 18 | 2 | {u'total_q_shots': 360, u'total_q_seconds': 18... | {u'vqe_output': {u'status': 0, u'success': Tru... | True | 18.754166 | 360 | 18.830697 | {u'status': 0, u'success': True, u'fun_validat... | ||||||
b7125fab439a9e3a | 0 | -1.12311 | -0.819667 | -1.123114e+00 | {u'maxfev': 10} | def my_minimizer( func, x0, my_args=(), my_opt... | 4 | 1 | {u'total_q_shots': 80, u'total_q_seconds': 5.7... | {u'vqe_output': {u'status': 0, u'success': Tru... | True | 5.756638 | 80 | 5.775856 | {u'status': 0, u'success': True, u'fun_validat... | |||||
1 | 1.11022e-16 | -0.087204 | 1.110223e-16 | {u'maxfev': 10} | def my_minimizer( func, x0, my_args=(), my_opt... | 18 | 2 | {u'total_q_shots': 360, u'total_q_seconds': 21... | {u'vqe_output': {u'status': 0, u'success': Tru... | True | 21.961633 | 360 | 22.053991 | {u'status': 0, u'success': True, u'fun_validat... | ||||||
2 | -0.561557 | -1.148247 | -1.123114e+00 | {u'maxfev': 10} | def my_minimizer( func, x0, my_args=(), my_opt... | 32 | 3 | {u'total_q_shots': 640, u'total_q_seconds': 35... | {u'vqe_output': {u'status': 0, u'success': Tru... | True | 35.738146 | 640 | 35.877645 | {u'status': 0, u'success': True, u'fun_validat... | ||||||
10 | 30 | 314e14a49a71437e | 0 | -2.80778 | -2.807768 | -2.807784e+00 | {u'maxfev': 30} | def my_minimizer( func, x0, my_args=(), my_opt... | 56 | 8 | {u'total_q_shots': 2240, u'total_q_seconds': 5... | {u'vqe_output': {u'status': 0, u'success': Tru... | True | 57.790582 | 2240 | 58.117994 | {u'status': 0, u'success': True, u'fun_validat... | |||
1 | -2.80778 | -2.692701 | -2.527006e+00 | {u'maxfev': 30} | def my_minimizer( func, x0, my_args=(), my_opt... | 50 | 9 | {u'total_q_shots': 2000, u'total_q_seconds': 5... | {u'vqe_output': {u'status': 8, u'success': Fal... | False | 52.262874 | 2000 | 52.563720 | {u'status': 8, u'success': False, u'fun_valida... | ||||||
2 | -2.80778 | -2.786659 | -2.807784e+00 | {u'maxfev': 30} | def my_minimizer( func, x0, my_args=(), my_opt... | 18 | -1 | {u'total_q_shots': 720, u'total_q_seconds': 19... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 19.518256 | 720 | 19.603808 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
50 | 30 | 491b2d69510a4ba8 | 0 | -0.870413 | -0.819667 | -7.300238e-01 | {u'maxfev': 30} | def my_minimizer( func, x0, my_args=(), my_opt... | 1 | 1 | {u'total_q_shots': 200, u'total_q_seconds': 1.... | {u'vqe_output': {u'fun_validated': -0.73002382... | False | 1.797571 | 200 | 1.808480 | {u'fun_validated': -0.73002382896, u'nfev': 1,... | |||
1 | -0.814257 | -0.819667 | -8.704130e-01 | {u'maxfev': 30} | def my_minimizer( func, x0, my_args=(), my_opt... | 1 | 1 | {u'total_q_shots': 200, u'total_q_seconds': 1.... | {u'vqe_output': {u'fun_validated': -0.87041302... | False | 1.481297 | 200 | 1.499486 | {u'fun_validated': -0.870413026837, u'nfev': 1... | ||||||
2 | -0.64579 | -0.819667 | -8.984909e-01 | {u'maxfev': 30} | def my_minimizer( func, x0, my_args=(), my_opt... | 1 | 1 | {u'total_q_shots': 200, u'total_q_seconds': 1.... | {u'vqe_output': {u'fun_validated': -0.89849086... | False | 1.931402 | 200 | 1.946541 | {u'fun_validated': -0.898490866413, u'nfev': 1... | ||||||
e9d6776bc4951ac8 | 0 | -0.78618 | -0.819667 | -7.300238e-01 | {u'maxfev': 30} | def my_minimizer( func, x0, my_args=(), my_opt... | 1 | 1 | {u'total_q_shots': 200, u'total_q_seconds': 1.... | {u'vqe_output': {u'fun_validated': -0.73002382... | False | 1.124802 | 200 | 1.142778 | {u'fun_validated': -0.73002382896, u'nfev': 1,... | |||||
1 | -0.64579 | -0.819667 | -6.457903e-01 | {u'maxfev': 30} | def my_minimizer( func, x0, my_args=(), my_opt... | 1 | 1 | {u'total_q_shots': 200, u'total_q_seconds': 0.... | {u'vqe_output': {u'fun_validated': -0.64579031... | False | 0.777182 | 200 | 0.781641 | {u'fun_validated': -0.645790310234, u'nfev': 1... | ||||||
2 | -0.870413 | -0.819667 | -7.861795e-01 | {u'maxfev': 30} | def my_minimizer( func, x0, my_args=(), my_opt... | 1 | 1 | {u'total_q_shots': 200, u'total_q_seconds': 1.... | {u'vqe_output': {u'fun_validated': -0.78617950... | False | 1.071683 | 200 | 1.075770 | {u'fun_validated': -0.786179508111, u'nfev': 1... | ||||||
ebd6924b74dc7a35 | 0 | -0.954647 | -0.819667 | -7.300238e-01 | {u'maxfev': 30} | def my_minimizer( func, x0, my_args=(), my_opt... | 1 | 1 | {u'total_q_shots': 200, u'total_q_seconds': 1.... | {u'vqe_output': {u'fun_validated': -0.73002382... | False | 1.132301 | 200 | 1.153318 | {u'fun_validated': -0.73002382896, u'nfev': 1,... | |||||
1 | -0.842335 | -0.819667 | -6.738681e-01 | {u'maxfev': 30} | def my_minimizer( func, x0, my_args=(), my_opt... | 1 | 1 | {u'total_q_shots': 200, u'total_q_seconds': 0.... | {u'vqe_output': {u'fun_validated': -0.67386814... | False | 0.804977 | 200 | 0.823108 | {u'fun_validated': -0.67386814981, u'nfev': 1,... | ||||||
2 | -0.814257 | -0.819667 | -7.861795e-01 | {u'maxfev': 30} | def my_minimizer( func, x0, my_args=(), my_opt... | 1 | 1 | {u'total_q_shots': 200, u'total_q_seconds': 1.... | {u'vqe_output': {u'fun_validated': -0.78617950... | False | 1.863419 | 200 | 1.877219 | {u'fun_validated': -0.786179508111, u'nfev': 1... | ||||||
team-04 | my_nelder_mead | 160 | 80 | 925f197b594663a6 | 0 | -1.38634 | -1.179546 | -1.289826e+00 | {u'maxfev': 80} | def my_nelder_mead( func, x0, my_args=(), my_o... | 80 | 36 | {u'total_q_shots': 51200, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 69.519755 | 51200 | 71.469008 | {u'status': 1, u'success': False, u'final_simp... | |
1 | -1.03537 | -0.821240 | -7.633663e-01 | {u'maxfev': 80} | def my_nelder_mead( func, x0, my_args=(), my_o... | 80 | 32 | {u'total_q_shots': 51200, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 75.788074 | 51200 | 77.751140 | {u'status': 1, u'success': False, u'final_simp... | ||||||
2 | -0.982724 | -0.819667 | -8.511095e-01 | {u'maxfev': 80} | def my_nelder_mead( func, x0, my_args=(), my_o... | 80 | 29 | {u'total_q_shots': 51200, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 70.651139 | 51200 | 72.397085 | {u'status': 1, u'success': False, u'final_simp... | ||||||
team-05 | my_nelder_mead | 80 | 50 | c5e24abc476f8367 | 0 | -1.22841 | -0.827657 | -6.317514e-01 | {u'maxfev': 50} | def my_nelder_mead( func, x0, my_args=(), my_o... | 50 | 20 | {u'total_q_shots': 16000, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 52.501330 | 16000 | 53.151564 | {u'status': 1, u'success': False, u'final_simp... | |
1 | -0.982724 | -0.788031 | -7.019460e-01 | {u'maxfev': 50} | def my_nelder_mead( func, x0, my_args=(), my_o... | 50 | 18 | {u'total_q_shots': 16000, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 52.298681 | 16000 | 52.976299 | {u'status': 1, u'success': False, u'final_simp... | ||||||
2 | -1.12311 | -0.851614 | -1.052919e+00 | {u'maxfev': 50} | def my_nelder_mead( func, x0, my_args=(), my_o... | 51 | 19 | {u'total_q_shots': 16320, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 50.094924 | 16320 | 50.729251 | {u'status': 1, u'success': False, u'final_simp... | ||||||
team-06 | my_cobyla | 75 | 80 | 393eeec44fb694bc | 0 | -2.80778 | -2.805324 | -2.807784e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 20 | -1 | {u'total_q_shots': 6000, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 27.253836 | 6000 | 27.332909 | {u'status': 1, u'maxcv': 0.0, u'success': True... | |
1 | -2.80778 | -2.803723 | -2.807784e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 19 | -1 | {u'total_q_shots': 5700, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 22.238256 | 5700 | 22.306707 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
2 | -2.80778 | -2.807386 | -2.807784e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 20 | -1 | {u'total_q_shots': 6000, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 21.026183 | 6000 | 21.094583 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
100 | 80 | 7229f2ac1a4f408f | 0 | -2.80778 | -2.807779 | -2.807784e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 20 | -1 | {u'total_q_shots': 8000, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 26.208402 | 8000 | 26.308550 | {u'status': 1, u'maxcv': 0.0, u'success': True... | |||
1 | -2.80778 | -2.807522 | -2.807784e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 20 | -1 | {u'total_q_shots': 8000, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 27.249033 | 8000 | 27.342741 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
2 | -2.80778 | -2.807435 | -2.807784e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 18 | -1 | {u'total_q_shots': 7200, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 24.288434 | 7200 | 24.364441 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
b4e80a074c144aeb | 0 | -2.80778 | -2.807777 | -2.807784e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 19 | -1 | {u'total_q_shots': 7600, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 22.026956 | 7600 | 22.112254 | {u'status': 1, u'maxcv': 0.0, u'success': True... | |||||
1 | -2.80778 | -2.799617 | -2.807784e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 22 | -1 | {u'total_q_shots': 8800, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 28.066350 | 8800 | 28.162913 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
2 | -2.80778 | -2.807776 | -2.807784e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 19 | -1 | {u'total_q_shots': 7600, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 26.128929 | 7600 | 26.213254 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
team-07 | my_cobyla | 150 | 80 | 114a1dbc72e659cb | 0 | -2.80778 | -2.806650 | -2.807784e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 18 | -1 | {u'total_q_shots': 10800, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 19.434766 | 10800 | 19.786985 | {u'status': 1, u'maxcv': 0.0, u'success': True... | |
1 | -2.80778 | -2.806223 | -2.807784e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 26 | -1 | {u'total_q_shots': 15600, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 26.386949 | 15600 | 26.682805 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
2 | -2.80778 | -2.806995 | -2.807784e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 19 | -1 | {u'total_q_shots': 11400, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 22.870383 | 11400 | 23.172649 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
my_conjuage | 50 | -1 | a0c1334b598d1d50 | 0 | -1.0108 | -0.819745 | -6.738681e-01 | {} | def my_conjuage( func, x0, my_args=(), my_opti... | 120 | 2 | {u'total_q_shots': 24000, u'total_q_seconds': ... | {u'vqe_output': {u'status': 2, u'success': Fal... | False | 122.091210 | 24000 | 122.585877 | {u'status': 2, u'success': False, u'fun_valida... | ||
1 | -1.03888 | -0.819666 | -5.615568e-01 | {} | def my_conjuage( func, x0, my_args=(), my_opti... | 107 | 1 | {u'total_q_shots': 21400, u'total_q_seconds': ... | {u'vqe_output': {u'status': 2, u'success': Fal... | False | 111.936443 | 21400 | 112.374312 | {u'status': 2, u'success': False, u'fun_valida... | ||||||
2 | -1.12311 | -0.819635 | -8.142573e-01 | {} | def my_conjuage( func, x0, my_args=(), my_opti... | 84 | 1 | {u'total_q_shots': 16800, u'total_q_seconds': ... | {u'vqe_output': {u'status': 2, u'success': Fal... | False | 72.925829 | 16800 | 73.240609 | {u'status': 2, u'success': False, u'fun_valida... | ||||||
my_nelder_mead | 150 | 80 | 9629cdd4afb92cba | 0 | -2.80778 | -2.804159 | -2.807784e+00 | {u'maxfev': 80} | def my_nelder_mead( func, x0, my_args=(), my_o... | 73 | 27 | {u'total_q_shots': 43800, u'total_q_seconds': ... | {u'vqe_output': {u'status': 0, u'success': Tru... | True | 67.130710 | 43800 | 68.315428 | {u'status': 0, u'success': True, u'final_simpl... | ||
1 | -1.08568 | -0.917140 | -9.546465e-01 | {u'maxfev': 80} | def my_nelder_mead( func, x0, my_args=(), my_o... | 83 | 31 | {u'total_q_shots': 49800, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 73.870701 | 49800 | 74.663146 | {u'status': 1, u'success': False, u'final_simp... | ||||||
2 | -1.0108 | -0.756597 | -8.797723e-01 | {u'maxfev': 80} | def my_nelder_mead( func, x0, my_args=(), my_o... | 83 | 33 | {u'total_q_shots': 49800, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 72.337525 | 49800 | 73.175967 | {u'status': 1, u'success': False, u'final_simp... | ||||||
team-08 | my_nelder_mead | 40 | 80 | 1f2618007bf47498 | 0 | -1.19331 | -0.756565 | -8.072379e-01 | {u'maxfev': 80} | def my_nelder_mead( func, x0, my_args=(), my_o... | 83 | 32 | {u'total_q_shots': 13280, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 74.766012 | 13280 | 75.229784 | {u'status': 1, u'success': False, u'final_simp... | |
1 | -1.40389 | -0.818282 | -8.774325e-01 | {u'maxfev': 80} | def my_nelder_mead( func, x0, my_args=(), my_o... | 81 | 32 | {u'total_q_shots': 12960, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 72.984496 | 12960 | 73.440981 | {u'status': 1, u'success': False, u'final_simp... | ||||||
2 | -1.47409 | -0.819643 | -1.193308e+00 | {u'maxfev': 80} | def my_nelder_mead( func, x0, my_args=(), my_o... | 81 | 31 | {u'total_q_shots': 12960, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 67.259213 | 12960 | 67.686557 | {u'status': 1, u'success': False, u'final_simp... | ||||||
100 | 80 | ec34936968a6ded1 | 0 | -0.968685 | -0.729564 | -8.704130e-01 | {u'maxfev': 80} | def my_nelder_mead( func, x0, my_args=(), my_o... | 80 | 34 | {u'total_q_shots': 32000, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 73.595248 | 32000 | 74.524417 | {u'status': 1, u'success': False, u'final_simp... | |||
1 | -2.80778 | -2.807766 | -2.807784e+00 | {u'maxfev': 80} | def my_nelder_mead( func, x0, my_args=(), my_o... | 68 | 24 | {u'total_q_shots': 27200, u'total_q_seconds': ... | {u'vqe_output': {u'status': 0, u'success': Tru... | True | 64.184833 | 27200 | 64.879539 | {u'status': 0, u'success': True, u'final_simpl... | ||||||
2 | -1.13715 | -0.869965 | -9.827244e-01 | {u'maxfev': 80} | def my_nelder_mead( func, x0, my_args=(), my_o... | 80 | 32 | {u'total_q_shots': 32000, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 69.947757 | 32000 | 70.805862 | {u'status': 1, u'success': False, u'final_simp... | ||||||
team-09 | my_cobyla | 2 | 80 | 250b2eb6b7477af5 | 0 | -2.80778 | -2.573627 | -2.105838e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 16 | -1 | {u'total_q_shots': 128, u'total_q_seconds': 17... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 17.010586 | 128 | 17.046697 | {u'status': 1, u'maxcv': 0.0, u'success': True... | |
1 | -2.80778 | -2.573724 | -2.807784e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 19 | -1 | {u'total_q_shots': 152, u'total_q_seconds': 19... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 19.580909 | 152 | 19.622992 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
2 | -1.40389 | -0.783735 | -7.019460e-01 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 29 | -1 | {u'total_q_shots': 232, u'total_q_seconds': 27... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 27.443289 | 232 | 27.509893 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
10 | 80 | a95e224f9d46c9bc | 0 | -2.52701 | -2.573711 | -2.527006e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 28 | -1 | {u'total_q_shots': 1120, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 27.753658 | 1120 | 27.851050 | {u'status': 1, u'maxcv': 0.0, u'success': True... | |||
1 | -2.80778 | -2.551940 | -2.807784e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 30 | -1 | {u'total_q_shots': 1200, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 29.581038 | 1200 | 29.684360 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
2 | -2.80778 | -2.807686 | -2.807784e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 20 | -1 | {u'total_q_shots': 800, u'total_q_seconds': 19... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 19.537763 | 800 | 19.608615 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
16 | 80 | ea30144a65b56ed1 | 0 | -2.28132 | -2.578505 | -2.456811e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 29 | -1 | {u'total_q_shots': 1856, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 26.471421 | 1856 | 26.594091 | {u'status': 1, u'maxcv': 0.0, u'success': True... | |||
1 | -2.80778 | -2.761423 | -2.807784e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 25 | -1 | {u'total_q_shots': 1600, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 24.645023 | 1600 | 24.749772 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
2 | -2.80778 | -2.764632 | -2.720041e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 25 | -1 | {u'total_q_shots': 1600, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 22.928624 | 1600 | 23.033360 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
18 | 80 | 629ceefcd1714629 | 0 | -2.80778 | -2.714764 | -2.807784e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 28 | -1 | {u'total_q_shots': 2016, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 27.140919 | 2016 | 27.265458 | {u'status': 1, u'maxcv': 0.0, u'success': True... | |||
1 | -2.80778 | -2.791071 | -2.807784e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 20 | -1 | {u'total_q_shots': 1440, u'total_q_seconds': 1... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 19.110111 | 1440 | 19.199880 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
2 | -2.80778 | -2.799237 | -2.807784e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 18 | -1 | {u'total_q_shots': 1296, u'total_q_seconds': 1... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 17.308141 | 1296 | 17.390592 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
20 | 80 | d44f28297a82c9e2 | 0 | -2.45681 | -2.573808 | -2.456811e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 28 | -1 | {u'total_q_shots': 2240, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 28.609930 | 2240 | 28.745027 | {u'status': 1, u'maxcv': 0.0, u'success': True... | |||
1 | -2.80778 | -2.801190 | -2.807784e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 21 | -1 | {u'total_q_shots': 1680, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 23.262115 | 1680 | 23.361448 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
2 | -2.80778 | -2.748890 | -2.807784e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 27 | -1 | {u'total_q_shots': 2160, u'total_q_seconds': 3... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 31.011846 | 2160 | 31.137416 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
40 | 80 | ab01a50c47a6ac7e | 0 | -2.80778 | -2.771239 | -2.772687e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 29 | -1 | {u'total_q_shots': 4640, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 27.940711 | 4640 | 28.160966 | {u'status': 1, u'maxcv': 0.0, u'success': True... | |||
1 | -2.80778 | -2.797675 | -2.807784e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 21 | -1 | {u'total_q_shots': 3360, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 20.822810 | 3360 | 20.979337 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
2 | -2.80778 | -2.807634 | -2.807784e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 18 | -1 | {u'total_q_shots': 2880, u'total_q_seconds': 1... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 17.168759 | 2880 | 17.315130 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
100 | 80 | 8549d1ba56d120a4 | 0 | -2.56912 | -2.663535 | -2.667395e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 27 | -1 | {u'total_q_shots': 10800, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 27.039123 | 10800 | 27.485235 | {u'status': 1, u'maxcv': 0.0, u'success': True... | |||
1 | -2.79375 | -2.785434 | -2.779706e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 27 | -1 | {u'total_q_shots': 10800, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 26.798613 | 10800 | 27.236923 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
2 | -2.80778 | -2.801491 | -2.793745e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 23 | -1 | {u'total_q_shots': 9200, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 22.348109 | 9200 | 22.717963 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
140 | 80 | 46f4687bd0fc29c9 | 0 | -2.80778 | -2.807758 | -2.807784e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 19 | -1 | {u'total_q_shots': 10640, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 22.064407 | 10640 | 22.464429 | {u'status': 1, u'maxcv': 0.0, u'success': True... | |||
1 | -2.80778 | -2.807742 | -2.807784e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 18 | -1 | {u'total_q_shots': 10080, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 20.499323 | 10080 | 20.878470 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
2 | -2.80778 | -2.807775 | -2.807784e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 20 | -1 | {u'total_q_shots': 11200, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 24.192151 | 11200 | 24.619158 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
200 | 80 | 045e5aad57c12b50 | 0 | -2.80778 | -2.807752 | -2.807784e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 20 | -1 | {u'total_q_shots': 16000, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 22.155682 | 16000 | 22.758196 | {u'status': 1, u'maxcv': 0.0, u'success': True... | |||
1 | -2.80778 | -2.804710 | -2.807784e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 21 | -1 | {u'total_q_shots': 16800, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 21.444761 | 16800 | 22.061672 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
2 | -2.80778 | -2.805026 | -2.807784e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 27 | -1 | {u'total_q_shots': 21600, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 28.415005 | 21600 | 29.214709 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
team-10 | my_cobyla | 100 | 80 | 78fc1d13c58f82a3 | 0 | -2.80778 | -2.779652 | -2.779706e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 30 | -1 | {u'total_q_shots': 12000, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 40.502860 | 12000 | 40.695197 | {u'status': 1, u'maxcv': 0.0, u'success': True... | |
1 | -2.80778 | -2.798862 | -2.807784e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 22 | -1 | {u'total_q_shots': 8800, u'total_q_seconds': 3... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 32.735909 | 8800 | 32.870234 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
2 | -2.80778 | -2.802571 | -2.807784e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 19 | -1 | {u'total_q_shots': 7600, u'total_q_seconds': 3... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 31.526623 | 7600 | 31.643700 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
team-11 | my_cobyla | 100 | 80 | 35ba631d4fd7a6fd | 0 | -2.80778 | -2.806813 | -2.807784e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 19 | -1 | {u'total_q_shots': 7600, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 23.740069 | 7600 | 23.962526 | {u'status': 1, u'maxcv': 0.0, u'success': True... | |
1 | -2.80778 | -2.806867 | -2.807784e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 22 | -1 | {u'total_q_shots': 8800, u'total_q_seconds': 3... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 30.742847 | 8800 | 30.989007 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
2 | -2.80778 | -2.804325 | -2.807784e+00 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 21 | -1 | {u'total_q_shots': 8400, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 27.821226 | 8400 | 28.057022 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
team-12 | my_cobyla | 1 | 1 | eade07d3047bdb24 | 0 | 1.11022e-16 | -0.819667 | 1.110223e-16 | {u'maxiter': 1} | def my_cobyla( func, x0, my_args=(), my_option... | 1 | -1 | {u'total_q_shots': 4, u'total_q_seconds': 1.06... | {u'vqe_output': {u'status': 2, u'maxcv': 0.0, ... | False | 1.067293 | 4 | 1.068910 | {u'status': 2, u'maxcv': 0.0, u'success': Fals... | |
1 | 1.11022e-16 | -0.819667 | 1.110223e-16 | {u'maxiter': 1} | def my_cobyla( func, x0, my_args=(), my_option... | 1 | -1 | {u'total_q_shots': 4, u'total_q_seconds': 0.72... | {u'vqe_output': {u'status': 2, u'maxcv': 0.0, ... | False | 0.726941 | 4 | 0.728611 | {u'status': 2, u'maxcv': 0.0, u'success': Fals... | ||||||
2 | 1.11022e-16 | -0.819667 | 1.110223e-16 | {u'maxiter': 1} | def my_cobyla( func, x0, my_args=(), my_option... | 1 | -1 | {u'total_q_shots': 4, u'total_q_seconds': 1.04... | {u'vqe_output': {u'status': 2, u'maxcv': 0.0, ... | False | 1.049236 | 4 | 1.050699 | {u'status': 2, u'maxcv': 0.0, u'success': Fals... | ||||||
my_minimizer | 1 | 1 | 019aa2420742c0a3 | 0 | 1.11022e-16 | -0.662150 | -1.403892e+00 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 15 | 1 | {u'total_q_shots': 8, u'total_q_seconds': 1.82... | {u'vqe_output': {u'fun_validated': -1.40389197... | False | 1.827070 | 8 | 1.907088 | {u'fun_validated': -1.40389197877, u'nfev': 15... | ||
1f72b8e0f40fdb44 | 0 | -2.80778 | -2.712761 | -2.807784e+00 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 70 | 1 | {u'total_q_shots': 284, u'total_q_seconds': 74... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 74.433188 | 284 | 116.380968 | {u'fun_validated': -2.80778395754, u'nfev': 70... | |||||
303840188a953656 | 0 | -2.80778 | -1.420659 | -2.807784e+00 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 15 | 1 | {u'total_q_shots': 56, u'total_q_seconds': 16.... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 16.379572 | 56 | 19.565907 | {u'fun_validated': -2.80778395754, u'nfev': 15... | |||||
3d09e495e79945dd | 0 | 1.11022e-16 | -1.200619 | 1.110223e-16 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 15 | 1 | {u'total_q_shots': 56, u'total_q_seconds': 14.... | {u'vqe_output': {u'fun_validated': 1.110223024... | False | 14.852203 | 56 | 18.717049 | {u'fun_validated': 1.11022302463e-16, u'nfev':... | |||||
5c2e4aa0abefcc7b | 0 | -2.80778 | -2.571739 | -2.807784e+00 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 65 | 1 | {u'total_q_shots': 264, u'total_q_seconds': 75... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 75.806631 | 264 | 176.285873 | {u'fun_validated': -2.80778395754, u'nfev': 65... | |||||
1 | -1.40389 | -1.154420 | 1.110223e-16 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 65 | 1 | {u'total_q_shots': 264, u'total_q_seconds': 68... | {u'vqe_output': {u'fun_validated': 1.110223024... | False | 68.489614 | 264 | 207.697087 | {u'fun_validated': 1.11022302463e-16, u'nfev':... | ||||||
2 | 1.11022e-16 | -2.501642 | -1.403892e+00 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 65 | 1 | {u'total_q_shots': 264, u'total_q_seconds': 66... | {u'vqe_output': {u'fun_validated': -1.40389197... | False | 66.346568 | 264 | 149.525844 | {u'fun_validated': -1.40389197877, u'nfev': 65... | ||||||
3 | -1.40389 | -1.967527 | -1.403892e+00 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 65 | 1 | {u'total_q_shots': 264, u'total_q_seconds': 66... | {u'vqe_output': {u'fun_validated': -1.40389197... | False | 66.245084 | 264 | 136.596465 | {u'fun_validated': -1.40389197877, u'nfev': 65... | ||||||
4 | 1.11022e-16 | -1.605563 | -1.403892e+00 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 65 | 1 | {u'total_q_shots': 264, u'total_q_seconds': 65... | {u'vqe_output': {u'fun_validated': -1.40389197... | False | 65.789749 | 264 | 96.354491 | {u'fun_validated': -1.40389197877, u'nfev': 65... | ||||||
5 | -2.80778 | -2.656383 | -2.807784e+00 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 65 | 1 | {u'total_q_shots': 264, u'total_q_seconds': 71... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 71.787024 | 264 | 140.640111 | {u'fun_validated': -2.80778395754, u'nfev': 65... | ||||||
6 | -2.80778 | -1.870784 | -2.807784e+00 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 65 | 1 | {u'total_q_shots': 264, u'total_q_seconds': 68... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 68.774470 | 264 | 115.926852 | {u'fun_validated': -2.80778395754, u'nfev': 65... | ||||||
7 | -1.40389 | -1.561347 | -2.807784e+00 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 65 | 1 | {u'total_q_shots': 264, u'total_q_seconds': 76... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 76.699340 | 264 | 123.179337 | {u'fun_validated': -2.80778395754, u'nfev': 65... | ||||||
8 | -2.80778 | -2.148739 | -2.807784e+00 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 65 | 1 | {u'total_q_shots': 264, u'total_q_seconds': 77... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 77.481008 | 264 | 116.792001 | {u'fun_validated': -2.80778395754, u'nfev': 65... | ||||||
9 | -1.40389 | -2.193398 | 1.110223e-16 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 65 | 1 | {u'total_q_shots': 264, u'total_q_seconds': 83... | {u'vqe_output': {u'fun_validated': 1.110223024... | False | 83.945064 | 264 | 136.219659 | {u'fun_validated': 1.11022302463e-16, u'nfev':... | ||||||
5f7608e149258788 | 0 | -1.40389 | -2.632671 | -1.403892e+00 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 15 | 1 | {u'total_q_shots': 8, u'total_q_seconds': 1.85... | {u'vqe_output': {u'fun_validated': -1.40389197... | False | 1.852804 | 8 | 1.986439 | {u'fun_validated': -1.40389197877, u'nfev': 15... | |||||
63942deb93ed4924 | 0 | -2.80778 | -2.572678 | -2.807784e+00 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 65 | 1 | {u'total_q_shots': 264, u'total_q_seconds': 90... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 90.951478 | 264 | 231.659713 | {u'fun_validated': -2.80778395754, u'nfev': 65... | |||||
79d974140d498e28 | 0 | 1.11022e-16 | -0.335635 | 1.110223e-16 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 15 | 1 | {u'total_q_shots': 8, u'total_q_seconds': 3.80... | {u'vqe_output': {u'fun_validated': 1.110223024... | False | 3.808583 | 8 | 3.899038 | {u'fun_validated': 1.11022302463e-16, u'nfev':... | |||||
90284c41b17ac7c1 | 0 | 1.11022e-16 | -0.109345 | 1.110223e-16 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 15 | 1 | {u'total_q_shots': 8, u'total_q_seconds': 1.87... | {u'vqe_output': {u'fun_validated': 1.110223024... | False | 1.878548 | 8 | 1.969678 | {u'fun_validated': 1.11022302463e-16, u'nfev':... | |||||
999f11859d44c8c9 | 0 | [3.4886765408761726, 1.5156803717638714] | -0.819667 | -1.403892e+00 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 15 | 1 | {u'total_q_shots': 64, u'total_q_seconds': 19.... | {u'vqe_output': {u'fun_validated': -1.40389197... | False | 19.147405 | 64 | 24.621683 | {u'fun_validated': -1.40389197877, u'nfev': 15... | |||||
1 | [5.070733890728206, 2.1975801009657125] | -0.819667 | 1.110223e-16 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 15 | 1 | {u'total_q_shots': 64, u'total_q_seconds': 19.... | {u'vqe_output': {u'fun_validated': 1.110223024... | False | 19.269741 | 64 | 23.218153 | {u'fun_validated': 1.11022302463e-16, u'nfev':... | ||||||
2 | [0.9961193939582954, 4.985770376291675] | -0.819667 | -2.807784e+00 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 15 | 1 | {u'total_q_shots': 64, u'total_q_seconds': 19.... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 19.340728 | 64 | 23.410579 | {u'fun_validated': -2.80778395754, u'nfev': 15... | ||||||
c24948fb82b1b9b2 | 0 | 1.11022e-16 | -1.224267 | -1.403892e+00 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 15 | 1 | {u'total_q_shots': 56, u'total_q_seconds': 15.... | {u'vqe_output': {u'fun_validated': -1.40389197... | False | 15.071416 | 56 | 193.410867 | {u'fun_validated': -1.40389197877, u'nfev': 15... | |||||
5 | 1 | 2d241af9ac061a2b | 0 | -2.80778 | -2.798363 | -2.807784e+00 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 200 | 1 | {u'total_q_shots': 820, u'total_q_seconds': 46... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 46.073689 | 820 | 72.821843 | {u'fun_validated': -2.80778395754, u'nfev': 20... | |||
10 | 1 | 334bbebf811298b1 | 0 | -2.80778 | -2.617267 | -2.386616e+00 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 15 | 1 | {u'total_q_shots': 1640, u'total_q_seconds': 3... | {u'vqe_output': {u'fun_validated': -2.38661636... | False | 38.805274 | 1640 | 57.918526 | {u'fun_validated': -2.38661636391, u'nfev': 15... | |||
3accdce0b47ad82d | 0 | -0.561557 | -0.443228 | -5.615568e-01 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 15 | 1 | {u'total_q_shots': 1640, u'total_q_seconds': 4... | {u'vqe_output': {u'fun_validated': -0.56155679... | False | 46.075820 | 1640 | 77.617802 | {u'fun_validated': -0.561556791508, u'nfev': 1... | |||||
7a3b8b947f7be3bf | 0 | -2.80778 | -2.794772 | -2.807784e+00 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 300 | 1 | {u'total_q_shots': 1240, u'total_q_seconds': 3... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 33.306817 | 1240 | 78.860508 | {u'fun_validated': -2.80778395754, u'nfev': 30... | |||||
84e8a43e384b275b | 0 | -2.80778 | -2.791795 | -2.807784e+00 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 400 | 1 | {u'total_q_shots': 1640, u'total_q_seconds': 4... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 43.331684 | 1640 | 61.679198 | {u'fun_validated': -2.80778395754, u'nfev': 40... | |||||
team-13 | my_grid_sampler | 50 | -1 | 80a6ae5dc4c96a97 | 0 | -2.80778 | -2.807768 | -2.807784e+00 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 9 | 9 | {u'total_q_shots': 2000, u'total_q_seconds': 1... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 10.851625 | 2000 | 10.909820 | {u'fun_validated': -2.80778395754, u'nfev': 9,... | |
1 | -2.80778 | -2.807778 | -2.807784e+00 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 9 | 9 | {u'total_q_shots': 2000, u'total_q_seconds': 1... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 13.178505 | 2000 | 13.242013 | {u'fun_validated': -2.80778395754, u'nfev': 9,... | ||||||
2 | -2.80778 | -2.807774 | -2.807784e+00 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 9 | 9 | {u'total_q_shots': 2000, u'total_q_seconds': 1... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 11.244428 | 2000 | 11.300547 | {u'fun_validated': -2.80778395754, u'nfev': 9,... | ||||||
3 | -2.80778 | -2.807780 | -2.807784e+00 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 9 | 9 | {u'total_q_shots': 2000, u'total_q_seconds': 1... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 12.802471 | 2000 | 12.853892 | {u'fun_validated': -2.80778395754, u'nfev': 9,... | ||||||
4 | -2.80778 | -2.807784 | -2.807784e+00 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 9 | 9 | {u'total_q_shots': 2000, u'total_q_seconds': 1... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 10.043252 | 2000 | 10.098364 | {u'fun_validated': -2.80778395754, u'nfev': 9,... | ||||||
5 | -2.80778 | -2.807765 | -2.807784e+00 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 9 | 9 | {u'total_q_shots': 2000, u'total_q_seconds': 9... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 9.215451 | 2000 | 9.268045 | {u'fun_validated': -2.80778395754, u'nfev': 9,... | ||||||
6 | -2.80778 | -2.807768 | -2.807784e+00 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 9 | 9 | {u'total_q_shots': 2000, u'total_q_seconds': 1... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 11.050707 | 2000 | 11.104310 | {u'fun_validated': -2.80778395754, u'nfev': 9,... | ||||||
7 | -2.80778 | -2.807776 | -2.807784e+00 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 9 | 9 | {u'total_q_shots': 2000, u'total_q_seconds': 1... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 10.822871 | 2000 | 10.874261 | {u'fun_validated': -2.80778395754, u'nfev': 9,... | ||||||
8 | -2.80778 | -2.807728 | -2.807784e+00 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 9 | 9 | {u'total_q_shots': 2000, u'total_q_seconds': 1... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 10.843415 | 2000 | 10.899113 | {u'fun_validated': -2.80778395754, u'nfev': 9,... | ||||||
9 | -2.80778 | -2.807780 | -2.807784e+00 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 9 | 9 | {u'total_q_shots': 2000, u'total_q_seconds': 1... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 10.079154 | 2000 | 10.135145 | {u'fun_validated': -2.80778395754, u'nfev': 9,... | ||||||
8a22024b968db35c | 0 | -2.80778 | -2.807773 | -2.807784e+00 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 9 | 9 | {u'total_q_shots': 2000, u'total_q_seconds': 1... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 11.487388 | 2000 | 11.540828 | {u'fun_validated': -2.80778395754, u'nfev': 9,... | |||||
9a184a90bb45cdfd | 0 | -2.80778 | -2.807558 | -2.807784e+00 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 25 | 25 | {u'total_q_shots': 5200, u'total_q_seconds': 2... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 26.778504 | 5200 | 26.906323 | {u'fun_validated': -2.80778395754, u'nfev': 25... | |||||
100 | -1 | 55dbcff148a0acb7 | 0 | -2.80778 | -2.807769 | -2.807784e+00 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 9 | 9 | {u'total_q_shots': 4000, u'total_q_seconds': 9... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 9.301604 | 4000 | 9.388565 | {u'fun_validated': -2.80778395754, u'nfev': 9,... | |||
a149b18c33b5fca3 | 0 | -2.80778 | -2.807777 | -2.807784e+00 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 25 | 25 | {u'total_q_shots': 10400, u'total_q_seconds': ... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 25.806349 | 10400 | 26.017306 | {u'fun_validated': -2.80778395754, u'nfev': 25... | |||||
my_nelder_mead | 100 | 20 | 4556421aaf9faf07 | 0 | -0.996763 | -0.835669 | -8.984909e-01 | {u'maxfev': 20} | def my_nelder_mead( func, x0, my_args=(), my_o... | 20 | 8 | {u'total_q_shots': 8000, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 21.688485 | 8000 | 21.813857 | {u'status': 1, u'success': False, u'final_simp... | ||
1 | -1.17927 | -0.987567 | -1.038880e+00 | {u'maxfev': 20} | def my_nelder_mead( func, x0, my_args=(), my_o... | 20 | 9 | {u'total_q_shots': 8000, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 20.904102 | 8000 | 21.031785 | {u'status': 1, u'success': False, u'final_simp... | ||||||
2 | -1.0108 | -0.851760 | -8.423352e-01 | {u'maxfev': 20} | def my_nelder_mead( func, x0, my_args=(), my_o... | 22 | 8 | {u'total_q_shots': 8800, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 24.712318 | 8800 | 24.860826 | {u'status': 1, u'success': False, u'final_simp... | ||||||
team-14 | my_minimizer | 100 | -1 | 570316f994d2da8d | 0 | 1.45153e-06 | -2.705648 | -2.751628e+00 | {} | def my_minimizer( func, x0, my_args=(), my_opt... | 5 | -1 | {u'total_q_shots': 2000, u'total_q_seconds': 3... | {u'vqe_output': {u'fun': 1.45153325048e-06, u'... | False | 3.937018 | 2000 | 5.233734 | {u'fun': 1.45153325048e-06, u'nfev': 5, u'fun_... | |
1 | 1.6753e-06 | -2.437472 | -2.512967e+00 | {} | def my_minimizer( func, x0, my_args=(), my_opt... | 5 | -1 | {u'total_q_shots': 2000, u'total_q_seconds': 5... | {u'vqe_output': {u'fun': 1.6753039269e-06, u'n... | False | 5.530155 | 2000 | 6.855214 | {u'fun': 1.6753039269e-06, u'nfev': 5, u'fun_v... | ||||||
2 | 1.37239e-06 | -2.698639 | -2.667395e+00 | {} | def my_minimizer( func, x0, my_args=(), my_opt... | 5 | -1 | {u'total_q_shots': 2000, u'total_q_seconds': 3... | {u'vqe_output': {u'fun': 1.3723925889e-06, u'n... | False | 3.805601 | 2000 | 5.166322 | {u'fun': 1.3723925889e-06, u'nfev': 5, u'fun_v... |
df = merge_experimental_results(df)
display_in_full(df)
[Warning] Cannot merge experiments as the minimizer source is different: [[u'8q-agave'], [u'team-13'], [u'my_grid_sampler'], [50], [-1], [u'6bc43f399ba11566'], [0]] [[u'8q-agave'], [u'team-13'], [u'my_grid_sampler'], [50], [-1], [u'b1c368d747a2b5fb'], [0]] [Warning] Cannot merge experiments as the minimizer source is different: [[u'qvm'], [u'team-02'], [u'my_minimizer'], [100], [1], [u'0acf6e59aa3474f9'], [2]] [[u'qvm'], [u'team-02'], [u'my_minimizer'], [100], [1], [u'320b44ecbb1ec4e2'], [0]] [Warning] Cannot merge experiments as the minimizer source is different: [[u'qvm'], [u'team-02'], [u'my_minimizer'], [100], [1], [u'320b44ecbb1ec4e2'], [0]] [[u'qvm'], [u'team-02'], [u'my_minimizer'], [100], [1], [u'4d6b91ed69ffc3ac'], [0]] [Warning] Cannot merge experiments as the minimizer source is different: [[u'qvm'], [u'team-02'], [u'my_minimizer'], [100], [1], [u'4d6b91ed69ffc3ac'], [0]] [[u'qvm'], [u'team-02'], [u'my_minimizer'], [100], [1], [u'8e654689bc7e50ae'], [0]] [Warning] Cannot merge experiments as the minimizer source is different: [[u'qvm'], [u'team-02'], [u'my_minimizer'], [100], [1], [u'8e654689bc7e50ae'], [0]] [[u'qvm'], [u'team-02'], [u'my_minimizer'], [100], [1], [u'd97add24d3da668c'], [0]] [Warning] Cannot merge experiments as the minimizer source is different: [[u'qvm'], [u'team-02'], [u'my_minimizer'], [100], [1], [u'd97add24d3da668c'], [2]] [[u'qvm'], [u'team-02'], [u'my_minimizer'], [100], [1], [u'e9eb662e8940a18f'], [3]] [Info] Merging experiment: [[u'qvm'], [u'team-03'], [u'my_cobyla'], [50], [30], [u'b1b31421eaffd55a'], [0]] [Info] into: [[u'qvm'], [u'team-03'], [u'my_cobyla'], [50], [30], [u'3c1a71fb284ef7a3'], [2]] [Info] as: [[u'qvm'], [u'team-03'], [u'my_cobyla'], [50], [30], [u'3c1a71fb284ef7a3'], [3]] [Info] Merging experiment: [[u'qvm'], [u'team-03'], [u'my_cobyla'], [50], [30], [u'b1b31421eaffd55a'], [1]] [Info] into: [[u'qvm'], [u'team-03'], [u'my_cobyla'], [50], [30], [u'3c1a71fb284ef7a3'], [3]] [Info] as: [[u'qvm'], [u'team-03'], [u'my_cobyla'], [50], [30], [u'3c1a71fb284ef7a3'], [4]] [Info] Merging experiment: [[u'qvm'], [u'team-03'], [u'my_cobyla'], [50], [30], [u'b1b31421eaffd55a'], [2]] [Info] into: [[u'qvm'], [u'team-03'], [u'my_cobyla'], [50], [30], [u'3c1a71fb284ef7a3'], [4]] [Info] as: [[u'qvm'], [u'team-03'], [u'my_cobyla'], [50], [30], [u'3c1a71fb284ef7a3'], [5]] [Info] Merging experiment: [[u'qvm'], [u'team-03'], [u'my_cobyla'], [50], [30], [u'cbfaa4139b8b0662'], [0]] [Info] into: [[u'qvm'], [u'team-03'], [u'my_cobyla'], [50], [30], [u'3c1a71fb284ef7a3'], [5]] [Info] as: [[u'qvm'], [u'team-03'], [u'my_cobyla'], [50], [30], [u'3c1a71fb284ef7a3'], [6]] [Info] Merging experiment: [[u'qvm'], [u'team-03'], [u'my_cobyla'], [50], [30], [u'cbfaa4139b8b0662'], [1]] [Info] into: [[u'qvm'], [u'team-03'], [u'my_cobyla'], [50], [30], [u'3c1a71fb284ef7a3'], [6]] [Info] as: [[u'qvm'], [u'team-03'], [u'my_cobyla'], [50], [30], [u'3c1a71fb284ef7a3'], [7]] [Info] Merging experiment: [[u'qvm'], [u'team-03'], [u'my_cobyla'], [50], [30], [u'cbfaa4139b8b0662'], [2]] [Info] into: [[u'qvm'], [u'team-03'], [u'my_cobyla'], [50], [30], [u'3c1a71fb284ef7a3'], [7]] [Info] as: [[u'qvm'], [u'team-03'], [u'my_cobyla'], [50], [30], [u'3c1a71fb284ef7a3'], [8]] [Warning] Cannot merge experiments as the minimizer source is different: [[u'qvm'], [u'team-03'], [u'my_minimizer'], [5], [10], [u'2b74910afe36e6b5'], [2]] [[u'qvm'], [u'team-03'], [u'my_minimizer'], [5], [10], [u'9f9bf704d869c823'], [0]] [Warning] Cannot merge experiments as the minimizer source is different: [[u'qvm'], [u'team-03'], [u'my_minimizer'], [5], [10], [u'9f9bf704d869c823'], [2]] [[u'qvm'], [u'team-03'], [u'my_minimizer'], [5], [10], [u'b7125fab439a9e3a'], [0]] [Info] Merging experiment: [[u'qvm'], [u'team-03'], [u'my_minimizer'], [50], [30], [u'e9d6776bc4951ac8'], [0]] [Info] into: [[u'qvm'], [u'team-03'], [u'my_minimizer'], [50], [30], [u'491b2d69510a4ba8'], [2]] [Info] as: [[u'qvm'], [u'team-03'], [u'my_minimizer'], [50], [30], [u'491b2d69510a4ba8'], [3]] [Info] Merging experiment: [[u'qvm'], [u'team-03'], [u'my_minimizer'], [50], [30], [u'e9d6776bc4951ac8'], [1]] [Info] into: [[u'qvm'], [u'team-03'], [u'my_minimizer'], [50], [30], [u'491b2d69510a4ba8'], [3]] [Info] as: [[u'qvm'], [u'team-03'], [u'my_minimizer'], [50], [30], [u'491b2d69510a4ba8'], [4]] [Info] Merging experiment: [[u'qvm'], [u'team-03'], [u'my_minimizer'], [50], [30], [u'e9d6776bc4951ac8'], [2]] [Info] into: [[u'qvm'], [u'team-03'], [u'my_minimizer'], [50], [30], [u'491b2d69510a4ba8'], [4]] [Info] as: [[u'qvm'], [u'team-03'], [u'my_minimizer'], [50], [30], [u'491b2d69510a4ba8'], [5]] [Info] Merging experiment: [[u'qvm'], [u'team-03'], [u'my_minimizer'], [50], [30], [u'ebd6924b74dc7a35'], [0]] [Info] into: [[u'qvm'], [u'team-03'], [u'my_minimizer'], [50], [30], [u'491b2d69510a4ba8'], [5]] [Info] as: [[u'qvm'], [u'team-03'], [u'my_minimizer'], [50], [30], [u'491b2d69510a4ba8'], [6]] [Info] Merging experiment: [[u'qvm'], [u'team-03'], [u'my_minimizer'], [50], [30], [u'ebd6924b74dc7a35'], [1]] [Info] into: [[u'qvm'], [u'team-03'], [u'my_minimizer'], [50], [30], [u'491b2d69510a4ba8'], [6]] [Info] as: [[u'qvm'], [u'team-03'], [u'my_minimizer'], [50], [30], [u'491b2d69510a4ba8'], [7]] [Info] Merging experiment: [[u'qvm'], [u'team-03'], [u'my_minimizer'], [50], [30], [u'ebd6924b74dc7a35'], [2]] [Info] into: [[u'qvm'], [u'team-03'], [u'my_minimizer'], [50], [30], [u'491b2d69510a4ba8'], [7]] [Info] as: [[u'qvm'], [u'team-03'], [u'my_minimizer'], [50], [30], [u'491b2d69510a4ba8'], [8]] [Info] Merging experiment: [[u'qvm'], [u'team-06'], [u'my_cobyla'], [100], [80], [u'b4e80a074c144aeb'], [0]] [Info] into: [[u'qvm'], [u'team-06'], [u'my_cobyla'], [100], [80], [u'7229f2ac1a4f408f'], [2]] [Info] as: [[u'qvm'], [u'team-06'], [u'my_cobyla'], [100], [80], [u'7229f2ac1a4f408f'], [3]] [Info] Merging experiment: [[u'qvm'], [u'team-06'], [u'my_cobyla'], [100], [80], [u'b4e80a074c144aeb'], [1]] [Info] into: [[u'qvm'], [u'team-06'], [u'my_cobyla'], [100], [80], [u'7229f2ac1a4f408f'], [3]] [Info] as: [[u'qvm'], [u'team-06'], [u'my_cobyla'], [100], [80], [u'7229f2ac1a4f408f'], [4]] [Info] Merging experiment: [[u'qvm'], [u'team-06'], [u'my_cobyla'], [100], [80], [u'b4e80a074c144aeb'], [2]] [Info] into: [[u'qvm'], [u'team-06'], [u'my_cobyla'], [100], [80], [u'7229f2ac1a4f408f'], [4]] [Info] as: [[u'qvm'], [u'team-06'], [u'my_cobyla'], [100], [80], [u'7229f2ac1a4f408f'], [5]] [Warning] Cannot merge experiments as the minimizer source is different: [[u'qvm'], [u'team-12'], [u'my_minimizer'], [1], [1], [u'019aa2420742c0a3'], [0]] [[u'qvm'], [u'team-12'], [u'my_minimizer'], [1], [1], [u'1f72b8e0f40fdb44'], [0]] [Warning] Cannot merge experiments as the minimizer source is different: [[u'qvm'], [u'team-12'], [u'my_minimizer'], [1], [1], [u'1f72b8e0f40fdb44'], [0]] [[u'qvm'], [u'team-12'], [u'my_minimizer'], [1], [1], [u'303840188a953656'], [0]] [Warning] Cannot merge experiments as the minimizer source is different: [[u'qvm'], [u'team-12'], [u'my_minimizer'], [1], [1], [u'303840188a953656'], [0]] [[u'qvm'], [u'team-12'], [u'my_minimizer'], [1], [1], [u'3d09e495e79945dd'], [0]] [Warning] Cannot merge experiments as the minimizer source is different: [[u'qvm'], [u'team-12'], [u'my_minimizer'], [1], [1], [u'3d09e495e79945dd'], [0]] [[u'qvm'], [u'team-12'], [u'my_minimizer'], [1], [1], [u'5c2e4aa0abefcc7b'], [0]] [Warning] Cannot merge experiments as the minimizer source is different: [[u'qvm'], [u'team-12'], [u'my_minimizer'], [1], [1], [u'5c2e4aa0abefcc7b'], [9]] [[u'qvm'], [u'team-12'], [u'my_minimizer'], [1], [1], [u'5f7608e149258788'], [0]] [Warning] Cannot merge experiments as the minimizer source is different: [[u'qvm'], [u'team-12'], [u'my_minimizer'], [1], [1], [u'5f7608e149258788'], [0]] [[u'qvm'], [u'team-12'], [u'my_minimizer'], [1], [1], [u'63942deb93ed4924'], [0]] [Warning] Cannot merge experiments as the minimizer source is different: [[u'qvm'], [u'team-12'], [u'my_minimizer'], [1], [1], [u'63942deb93ed4924'], [0]] [[u'qvm'], [u'team-12'], [u'my_minimizer'], [1], [1], [u'79d974140d498e28'], [0]] [Info] Merging experiment: [[u'qvm'], [u'team-12'], [u'my_minimizer'], [1], [1], [u'90284c41b17ac7c1'], [0]] [Info] into: [[u'qvm'], [u'team-12'], [u'my_minimizer'], [1], [1], [u'79d974140d498e28'], [0]] [Info] as: [[u'qvm'], [u'team-12'], [u'my_minimizer'], [1], [1], [u'79d974140d498e28'], [1]] [Warning] Cannot merge experiments as the minimizer source is different: [[u'qvm'], [u'team-12'], [u'my_minimizer'], [1], [1], [u'79d974140d498e28'], [1]] [[u'qvm'], [u'team-12'], [u'my_minimizer'], [1], [1], [u'999f11859d44c8c9'], [0]] [Warning] Cannot merge experiments as the minimizer source is different: [[u'qvm'], [u'team-12'], [u'my_minimizer'], [1], [1], [u'999f11859d44c8c9'], [2]] [[u'qvm'], [u'team-12'], [u'my_minimizer'], [1], [1], [u'c24948fb82b1b9b2'], [0]] [Warning] Cannot merge experiments as the minimizer source is different: [[u'qvm'], [u'team-12'], [u'my_minimizer'], [10], [1], [u'334bbebf811298b1'], [0]] [[u'qvm'], [u'team-12'], [u'my_minimizer'], [10], [1], [u'3accdce0b47ad82d'], [0]] [Warning] Cannot merge experiments as the minimizer source is different: [[u'qvm'], [u'team-12'], [u'my_minimizer'], [10], [1], [u'3accdce0b47ad82d'], [0]] [[u'qvm'], [u'team-12'], [u'my_minimizer'], [10], [1], [u'7a3b8b947f7be3bf'], [0]] [Warning] Cannot merge experiments as the minimizer source is different: [[u'qvm'], [u'team-12'], [u'my_minimizer'], [10], [1], [u'7a3b8b947f7be3bf'], [0]] [[u'qvm'], [u'team-12'], [u'my_minimizer'], [10], [1], [u'84e8a43e384b275b'], [0]] [Info] Merging experiment: [[u'qvm'], [u'team-13'], [u'my_grid_sampler'], [50], [-1], [u'8a22024b968db35c'], [0]] [Info] into: [[u'qvm'], [u'team-13'], [u'my_grid_sampler'], [50], [-1], [u'80a6ae5dc4c96a97'], [9]] [Info] as: [[u'qvm'], [u'team-13'], [u'my_grid_sampler'], [50], [-1], [u'80a6ae5dc4c96a97'], [10]] [Warning] Cannot merge experiments as the minimizer source is different: [[u'qvm'], [u'team-13'], [u'my_grid_sampler'], [50], [-1], [u'80a6ae5dc4c96a97'], [10]] [[u'qvm'], [u'team-13'], [u'my_grid_sampler'], [50], [-1], [u'9a184a90bb45cdfd'], [0]] [Warning] Cannot merge experiments as the minimizer source is different: [[u'qvm'], [u'team-13'], [u'my_grid_sampler'], [100], [-1], [u'55dbcff148a0acb7'], [0]] [[u'qvm'], [u'team-13'], [u'my_grid_sampler'], [100], [-1], [u'a149b18c33b5fca3'], [0]]
fun | fun_exact | fun_validated | minimizer_options | minimizer_src | nfev | nit | report | run | success | total_q_seconds | total_q_shots | total_seconds | vqe_output | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
platform | team | minimizer_method | sample_number | max_iterations | point | repetition_id | ||||||||||||||
8q-agave | team-02 | my_minimizer_old | 100 | -1 | 3674f6d98ee1bd3d | 0 | -0.588061 | -0.89543 | -0.995302 | {} | def my_minimizer_old( func, x0, my_args=(), my... | 1 | 1 | {u'total_q_shots': 3600, u'total_q_seconds': 2... | {u'vqe_output': {u'fun_validated': -0.99530180... | False | 200.16 | 3600 | 200.403 | {u'fun_validated': -0.995301805612, u'nfev': 1... |
1 | -0.352993 | -0.238422 | -0.981486 | {} | def my_minimizer_old( func, x0, my_args=(), my... | 1 | 1 | {u'total_q_shots': 3600, u'total_q_seconds': 7... | {u'vqe_output': {u'fun_validated': -0.98148640... | False | 70.9257 | 3600 | 71.1562 | {u'fun_validated': -0.981486402517, u'nfev': 1... | ||||||
2 | -1.14816 | -0.819667 | -1.22363 | {} | def my_minimizer_old( func, x0, my_args=(), my... | 1 | 1 | {u'total_q_shots': 3600, u'total_q_seconds': 7... | {u'vqe_output': {u'fun_validated': -1.22362982... | False | 74.2028 | 3600 | 74.4474 | {u'fun_validated': -1.22362982927, u'nfev': 1,... | ||||||
my_nelder_mead | 100 | 80 | e9eb662e8940a18f | 0 | -1.14804 | -0.778078 | -0.995414 | {u'maxfev': 80} | def my_nelder_mead( func, x0, my_args=(), my_o... | 81 | 28 | {u'total_q_shots': 32400, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 620.499 | 32400 | 683.715 | {u'status': 1, u'success': False, u'final_simp... | ||
1 | -1.29899 | -0.803944 | -0.835707 | {u'maxfev': 80} | def my_nelder_mead( func, x0, my_args=(), my_o... | 83 | 31 | {u'total_q_shots': 33200, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 790.792 | 33200 | 792.953 | {u'status': 1, u'success': False, u'final_simp... | ||||||
2 | -1.14108 | -0.695143 | -0.67061 | {u'maxfev': 80} | def my_nelder_mead( func, x0, my_args=(), my_o... | 82 | 30 | {u'total_q_shots': 32800, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 770.338 | 32800 | 835.876 | {u'status': 1, u'success': False, u'final_simp... | ||||||
team-07 | my_cobyla | 50 | 80 | 1de5308445c0a7c2 | 0 | -2.45737 | -2.7011 | -2.46793 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 33 | -1 | {u'total_q_shots': 6600, u'total_q_seconds': 1... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 199.525 | 6600 | 199.684 | {u'status': 1, u'maxcv': 0.0, u'success': True... | |
1 | -2.17659 | -2.7052 | -2.34506 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 33 | -1 | {u'total_q_shots': 6600, u'total_q_seconds': 1... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 195.105 | 6600 | 225.499 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
2 | -2.2433 | -2.56805 | -2.46793 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 28 | -1 | {u'total_q_shots': 5600, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 222.429 | 5600 | 222.559 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
150 | 80 | 27bd6b1f8844e4a4 | 0 | -2.19651 | -2.80078 | -2.20924 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 32 | -1 | {u'total_q_shots': 19200, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 253.491 | 19200 | 253.973 | {u'status': 1, u'maxcv': 0.0, u'success': True... | |||
1 | -2.20819 | -2.79951 | -2.27849 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 31 | -1 | {u'total_q_shots': 18600, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 236.087 | 18600 | 327.784 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
2 | -2.0234 | -2.80312 | -2.23514 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 31 | -1 | {u'total_q_shots': 18600, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 220.162 | 18600 | 220.468 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
300 | 80 | 24e4f93212e61359 | 0 | -2.21927 | -2.80012 | -2.19175 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 29 | -1 | {u'total_q_shots': 34800, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 481.251 | 34800 | 481.905 | {u'status': 1, u'maxcv': 0.0, u'success': True... | |||
1 | -2.02209 | -2.53406 | -2.20583 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 31 | -1 | {u'total_q_shots': 37200, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 210.81 | 37200 | 211.434 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
2 | -2.26135 | -2.80609 | -2.33971 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 29 | -1 | {u'total_q_shots': 34800, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 213.47 | 34800 | 214.155 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
my_nelder_mead | 150 | 80 | d4c9eba098e71597 | 0 | -1.10941 | -0.85176 | -0.977257 | {u'maxfev': 80} | def my_nelder_mead( func, x0, my_args=(), my_o... | 80 | 34 | {u'total_q_shots': 48000, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 612.524 | 48000 | 707.194 | {u'status': 1, u'success': False, u'final_simp... | ||
1 | -1.26163 | -0.948764 | -1.12236 | {u'maxfev': 80} | def my_nelder_mead( func, x0, my_args=(), my_o... | 80 | 32 | {u'total_q_shots': 48000, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 640.798 | 48000 | 641.994 | {u'status': 1, u'success': False, u'final_simp... | ||||||
2 | -1.22883 | -0.839324 | -1.04532 | {u'maxfev': 80} | def my_nelder_mead( func, x0, my_args=(), my_o... | 80 | 29 | {u'total_q_shots': 48000, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 692.888 | 48000 | 694.107 | {u'status': 1, u'success': False, u'final_simp... | ||||||
team-10 | my_minimizer | 1 | 8 | 225212636444a960 | 0 | -2.80778 | -2.74963 | -2.80778 | {u'maxfev': 8} | def my_minimizer( func, x0, my_args=(), my_opt... | 8 | 8 | {u'total_q_shots': 32, u'total_q_seconds': 69.... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 69.9116 | 32 | 100.557 | {u'fun_validated': -2.80778395754, u'nfev': 8,... | |
1 | -3.33564 | -2.57288 | -1.40389 | {u'maxfev': 8} | def my_minimizer( func, x0, my_args=(), my_opt... | 8 | 8 | {u'total_q_shots': 32, u'total_q_seconds': 60.... | {u'vqe_output': {u'fun_validated': -1.40389197... | False | 60.8164 | 32 | 60.8258 | {u'fun_validated': -1.40389197877, u'nfev': 8,... | ||||||
2 | -2.80778 | -2.6881 | -2.80778 | {u'maxfev': 8} | def my_minimizer( func, x0, my_args=(), my_opt... | 8 | 8 | {u'total_q_shots': 32, u'total_q_seconds': 46.... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 46.7979 | 32 | 46.8075 | {u'fun_validated': -2.80778395754, u'nfev': 8,... | ||||||
10 | 8 | a3842f89879d0961 | 0 | -3.01893 | -2.43453 | -2.51016 | {u'maxfev': 8} | def my_minimizer( func, x0, my_args=(), my_opt... | 8 | 8 | {u'total_q_shots': 320, u'total_q_seconds': 49... | {u'vqe_output': {u'fun_validated': -2.51015540... | False | 49.4448 | 320 | 49.4587 | {u'fun_validated': -2.51015540172, u'nfev': 8,... | |||
1 | -2.22938 | -2.71485 | -2.36977 | {u'maxfev': 8} | def my_minimizer( func, x0, my_args=(), my_opt... | 8 | 8 | {u'total_q_shots': 320, u'total_q_seconds': 10... | {u'vqe_output': {u'fun_validated': -2.36976620... | False | 109.043 | 320 | 109.057 | {u'fun_validated': -2.36976620384, u'nfev': 8,... | ||||||
2 | -2.51016 | -2.49231 | -1.79024 | {u'maxfev': 8} | def my_minimizer( func, x0, my_args=(), my_opt... | 8 | 8 | {u'total_q_shots': 320, u'total_q_seconds': 51... | {u'vqe_output': {u'fun_validated': -1.79024166... | False | 51.8971 | 320 | 82.2221 | {u'fun_validated': -1.7902416688, u'nfev': 8, ... | ||||||
my_nelder_mead | 1 | 8 | f6a07a94b24cf38d | 0 | -1.93175 | -0.787939 | 1.11022e-16 | {u'maxfev': 8} | def my_nelder_mead( func, x0, my_args=(), my_o... | 11 | 3 | {u'total_q_shots': 44, u'total_q_seconds': 80.... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 80.6617 | 44 | 80.6916 | {u'status': 1, u'success': False, u'final_simp... | ||
1 | -2.80778 | -0.756596 | 1.11022e-16 | {u'maxfev': 8} | def my_nelder_mead( func, x0, my_args=(), my_o... | 11 | 3 | {u'total_q_shots': 44, u'total_q_seconds': 80.... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 80.049 | 44 | 80.0784 | {u'status': 1, u'success': False, u'final_simp... | ||||||
2 | -2.80778 | -0.787939 | 1.11022e-16 | {u'maxfev': 8} | def my_nelder_mead( func, x0, my_args=(), my_o... | 9 | 3 | {u'total_q_shots': 36, u'total_q_seconds': 70.... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 70.7367 | 36 | 70.7582 | {u'status': 1, u'success': False, u'final_simp... | ||||||
100 | 80 | 4485d80395eb5874 | 0 | -1.19398 | -0.778102 | -1.08144 | {u'maxfev': 80} | def my_nelder_mead( func, x0, my_args=(), my_o... | 81 | 31 | {u'total_q_shots': 32400, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 619.284 | 32400 | 651.034 | {u'status': 1, u'success': False, u'final_simp... | |||
1 | -1.2343 | -0.76829 | -0.969021 | {u'maxfev': 80} | def my_nelder_mead( func, x0, my_args=(), my_o... | 80 | 32 | {u'total_q_shots': 32000, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 588.112 | 32000 | 588.585 | {u'status': 1, u'success': False, u'final_simp... | ||||||
2 | -1.13064 | -0.819667 | -0.969021 | {u'maxfev': 80} | def my_nelder_mead( func, x0, my_args=(), my_o... | 80 | 31 | {u'total_q_shots': 32000, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 593.561 | 32000 | 594.025 | {u'status': 1, u'success': False, u'final_simp... | ||||||
my_random_sampler | 100 | 10 | 251cad05f514492a | 0 | -2.25723 | -2.41585 | -1.94478 | {u'maxfev': 10} | def my_random_sampler( func, x0, my_args=(), m... | 10 | 10 | {u'total_q_shots': 4000, u'total_q_seconds': 6... | {u'vqe_output': {u'fun_validated': -1.94478154... | False | 66.2031 | 4000 | 66.2634 | {u'fun_validated': -1.94478154481, u'nfev': 10... | ||
1 | -2.16244 | -2.7313 | -2.20287 | {u'maxfev': 10} | def my_random_sampler( func, x0, my_args=(), m... | 10 | 10 | {u'total_q_shots': 4000, u'total_q_seconds': 8... | {u'vqe_output': {u'fun_validated': -2.20287242... | False | 80.0502 | 4000 | 80.1096 | {u'fun_validated': -2.20287242405, u'nfev': 10... | ||||||
2 | -2.25566 | -2.62823 | -2.1502 | {u'maxfev': 10} | def my_random_sampler( func, x0, my_args=(), m... | 10 | 10 | {u'total_q_shots': 4000, u'total_q_seconds': 1... | {u'vqe_output': {u'fun_validated': -2.15019853... | False | 139.305 | 4000 | 139.368 | {u'fun_validated': -2.15019853526, u'nfev': 10... | ||||||
team-12 | my_minimizer | 1 | 1 | 3174739978ea2d37 | 0 | -1.40389 | -2.0683 | -1.40389 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 70 | 1 | {u'total_q_shots': 284, u'total_q_seconds': 58... | {u'vqe_output': {u'fun_validated': -1.40389197... | False | 586.032 | 284 | 636.83 | {u'fun_validated': -1.40389197877, u'nfev': 70... | |
5 | 1 | 827f495dba576e4a | 0 | -2.00138 | -2.69919 | -2.73815 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 200 | 1 | {u'total_q_shots': 820, u'total_q_seconds': 25... | {u'vqe_output': {u'fun_validated': -2.73814815... | False | 251.748 | 820 | 268.702 | {u'fun_validated': -2.73814815033, u'nfev': 20... | |||
team-13 | my_grid_sampler | 50 | -1 | 6bc43f399ba11566 | 0 | -2.55553 | -2.80775 | -2.47826 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 9 | 9 | {u'total_q_shots': 2000, u'total_q_seconds': 5... | {u'vqe_output': {u'fun_validated': -2.47826049... | False | 56.1474 | 2000 | 56.2183 | {u'fun_validated': -2.47826049674, u'nfev': 9,... | |
b1c368d747a2b5fb | 0 | -1.93422 | -2.74953 | -2.40458 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 25 | 25 | {u'total_q_shots': 5200, u'total_q_seconds': 1... | {u'vqe_output': {u'fun_validated': -2.40458410... | False | 146.762 | 5200 | 146.884 | {u'fun_validated': -2.40458410744, u'nfev': 25... | |||||
100 | -1 | 6f64ff6b83067e89 | 0 | -2.32731 | -2.80776 | -2.26936 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 9 | 9 | {u'total_q_shots': 4000, u'total_q_seconds': 5... | {u'vqe_output': {u'fun_validated': -2.26936171... | False | 57.7656 | 4000 | 57.8576 | {u'fun_validated': -2.26936171593, u'nfev': 9,... | |||
qvm | team-01 | my_cobyla | 50 | 80 | c6e2a72de42fba64 | 0 | -2.80778 | -2.80287 | -2.80778 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 22 | -1 | {u'total_q_shots': 4400, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 20.5322 | 4400 | 20.6761 | {u'status': 1, u'maxcv': 0.0, u'success': True... |
1 | -2.63932 | -2.7503 | -2.75163 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 33 | -1 | {u'total_q_shots': 6600, u'total_q_seconds': 3... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 31.8539 | 6600 | 32.0151 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
2 | -2.75163 | -2.75595 | -2.75163 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 31 | -1 | {u'total_q_shots': 6200, u'total_q_seconds': 3... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 35.0153 | 6200 | 35.164 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
my_grid_sampler | 10 | -1 | 9dc38b214d7fbb38 | 0 | -2.80778 | -2.80778 | -2.80778 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 10 | 10 | {u'total_q_shots': 4000, u'total_q_seconds': 1... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 107.699 | 4000 | 107.935 | {u'fun_validated': -2.80778395754, u'nfev': 10... | ||
1 | -2.80778 | -2.80778 | -2.80778 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 10 | 10 | {u'total_q_shots': 4000, u'total_q_seconds': 9... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 95.9064 | 4000 | 96.0992 | {u'fun_validated': -2.80778395754, u'nfev': 10... | ||||||
2 | -2.80778 | -2.80778 | -2.80778 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 10 | 10 | {u'total_q_shots': 4000, u'total_q_seconds': 1... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 104.597 | 4000 | 104.785 | {u'fun_validated': -2.80778395754, u'nfev': 10... | ||||||
team-02 | my_minimizer | 100 | 1 | 0acf6e59aa3474f9 | 0 | -0.715985 | -0.819667 | -0.730024 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 1 | 1 | {u'total_q_shots': 400, u'total_q_seconds': 1.... | {u'vqe_output': {u'fun_validated': -0.73002382... | False | 1.47761 | 400 | 1.50858 | {u'fun_validated': -0.73002382896, u'nfev': 1,... | |
1 | -0.842335 | -0.819667 | -0.730024 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 1 | 1 | {u'total_q_shots': 400, u'total_q_seconds': 2.... | {u'vqe_output': {u'fun_validated': -0.73002382... | False | 2.48925 | 400 | 2.52185 | {u'fun_validated': -0.73002382896, u'nfev': 1,... | ||||||
2 | -0.701946 | -0.819667 | -0.758102 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 1 | 1 | {u'total_q_shots': 400, u'total_q_seconds': 1.... | {u'vqe_output': {u'fun_validated': -0.75810166... | False | 1.144 | 400 | 1.17834 | {u'fun_validated': -0.758101668536, u'nfev': 1... | ||||||
320b44ecbb1ec4e2 | 0 | -0.744063 | -0.819667 | -0.715985 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 1 | 1 | {u'total_q_shots': 3600, u'total_q_seconds': 1... | {u'vqe_output': {u'fun_validated': -0.71598490... | False | 10.1031 | 3600 | 10.3709 | {u'fun_validated': -0.715984909173, u'nfev': 1... | |||||
4d6b91ed69ffc3ac | 0 | -0.814257 | -0.819667 | -0.744063 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 1 | 1 | {u'total_q_shots': 3600, u'total_q_seconds': 1... | {u'vqe_output': {u'fun_validated': -0.74406274... | False | 10.1789 | 3600 | 10.4253 | {u'fun_validated': -0.744062748748, u'nfev': 1... | |||||
8e654689bc7e50ae | 0 | -0.814257 | -0.819667 | -0.856374 | {u'maxfev': 1} | def my_minimizer(func, x0, my_args=(), my_opti... | 1 | 1 | {u'total_q_shots': 400, u'total_q_seconds': 1.... | {u'vqe_output': {u'fun_validated': -0.85637410... | False | 1.34232 | 400 | 1.36885 | {u'fun_validated': -0.85637410705, u'nfev': 1,... | |||||
d97add24d3da668c | 0 | 1.11022e-16 | -0.699686 | -0.842335 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 1 | 1 | {u'total_q_shots': 3600, u'total_q_seconds': 8... | {u'vqe_output': {u'fun_validated': -0.84233518... | False | 8.72486 | 3600 | 8.99995 | {u'fun_validated': -0.842335187262, u'nfev': 1... | |||||
1 | -0.730024 | -0.819667 | -0.715985 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 1 | 1 | {u'total_q_shots': 3600, u'total_q_seconds': 1... | {u'vqe_output': {u'fun_validated': -0.71598490... | False | 10.7677 | 3600 | 11.0355 | {u'fun_validated': -0.715984909173, u'nfev': 1... | ||||||
2 | -0.280778 | -1.50454 | -1.44601 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 1 | 1 | {u'total_q_shots': 3600, u'total_q_seconds': 9... | {u'vqe_output': {u'fun_validated': -1.44600873... | False | 9.31793 | 3600 | 9.58397 | {u'fun_validated': -1.44600873813, u'nfev': 1,... | ||||||
e9eb662e8940a18f | 3 | -0.744063 | -0.819667 | -0.715985 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 1 | 1 | {u'total_q_shots': 3600, u'total_q_seconds': 1... | {u'vqe_output': {u'fun_validated': -0.71598490... | False | 10.1031 | 3600 | 10.3709 | {u'fun_validated': -0.715984909173, u'nfev': 1... | |||||
my_minimizer_old | 100 | -1 | 4f31e741e5931b12 | 0 | 1.11022e-16 | -1.00614 | -0.828296 | {} | def my_minimizer_old( func, x0, my_args=(), my... | 1 | 1 | {u'total_q_shots': 3600, u'total_q_seconds': 8... | {u'vqe_output': {u'fun_validated': -0.82829626... | False | 8.73975 | 3600 | 9.00106 | {u'fun_validated': -0.828296267474, u'nfev': 1... | ||
1 | -0.112311 | -0.107069 | -0.0701946 | {} | def my_minimizer_old( func, x0, my_args=(), my... | 1 | 1 | {u'total_q_shots': 3600, u'total_q_seconds': 9... | {u'vqe_output': {u'fun_validated': -0.07019459... | False | 9.49927 | 3600 | 9.75163 | {u'fun_validated': -0.0701945989385, u'nfev': ... | ||||||
2 | -0.687907 | -0.819667 | -0.982724 | {} | def my_minimizer_old( func, x0, my_args=(), my... | 1 | 1 | {u'total_q_shots': 3600, u'total_q_seconds': 9... | {u'vqe_output': {u'fun_validated': -0.98272438... | False | 9.29423 | 3600 | 9.53397 | {u'fun_validated': -0.982724385139, u'nfev': 1... | ||||||
team-03 | my_cobyla | 2 | 3 | 240e78c79fe4651a | 0 | 1.11022e-16 | -0.819667 | 1.11022e-16 | {u'maxiter': 3} | def my_cobyla( func, x0, my_args=(), my_option... | 3 | -1 | {u'total_q_shots': 24, u'total_q_seconds': 2.7... | {u'vqe_output': {u'status': 2, u'maxcv': 0.0, ... | False | 2.71955 | 24 | 2.72926 | {u'status': 2, u'maxcv': 0.0, u'success': Fals... | |
1 | -0.701946 | -0.819667 | -1.40389 | {u'maxiter': 3} | def my_cobyla( func, x0, my_args=(), my_option... | 3 | -1 | {u'total_q_shots': 24, u'total_q_seconds': 2.4... | {u'vqe_output': {u'status': 2, u'maxcv': 0.0, ... | False | 2.43012 | 24 | 2.44595 | {u'status': 2, u'maxcv': 0.0, u'success': Fals... | ||||||
2 | -0.701946 | -0.819667 | 1.11022e-16 | {u'maxiter': 3} | def my_cobyla( func, x0, my_args=(), my_option... | 3 | -1 | {u'total_q_shots': 24, u'total_q_seconds': 3.1... | {u'vqe_output': {u'status': 2, u'maxcv': 0.0, ... | False | 3.19008 | 24 | 3.20538 | {u'status': 2, u'maxcv': 0.0, u'success': Fals... | ||||||
30 | e704e00811f08d88 | 0 | -2.80778 | -2.57372 | -2.80778 | {u'maxiter': 30} | def my_cobyla( func, x0, my_args=(), my_option... | 15 | -1 | {u'total_q_shots': 120, u'total_q_seconds': 19... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 19.2759 | 120 | 19.3259 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||
1 | -2.10584 | -2.57363 | -2.80778 | {u'maxiter': 30} | def my_cobyla( func, x0, my_args=(), my_option... | 18 | -1 | {u'total_q_shots': 144, u'total_q_seconds': 21... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 21.8421 | 144 | 21.9087 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
2 | -0.701946 | -0.664905 | -0.701946 | {u'maxiter': 30} | def my_cobyla( func, x0, my_args=(), my_option... | 27 | -1 | {u'total_q_shots': 216, u'total_q_seconds': 31... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 31.6906 | 216 | 31.793 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
5 | 10 | aa64cff4b1d94d38 | 0 | -2.80778 | -2.57372 | -2.80778 | {u'maxiter': 10} | def my_cobyla( func, x0, my_args=(), my_option... | 10 | -1 | {u'total_q_shots': 200, u'total_q_seconds': 8.... | {u'vqe_output': {u'status': 2, u'maxcv': 0.0, ... | False | 8.9148 | 200 | 8.96425 | {u'status': 2, u'maxcv': 0.0, u'success': Fals... | |||
1 | -2.80778 | -2.57372 | -2.52701 | {u'maxiter': 10} | def my_cobyla( func, x0, my_args=(), my_option... | 10 | -1 | {u'total_q_shots': 200, u'total_q_seconds': 8.... | {u'vqe_output': {u'status': 2, u'maxcv': 0.0, ... | False | 8.49102 | 200 | 8.55271 | {u'status': 2, u'maxcv': 0.0, u'success': Fals... | ||||||
2 | -2.80778 | -2.80174 | -2.80778 | {u'maxiter': 10} | def my_cobyla( func, x0, my_args=(), my_option... | 10 | -1 | {u'total_q_shots': 200, u'total_q_seconds': 8.... | {u'vqe_output': {u'status': 2, u'maxcv': 0.0, ... | False | 8.07 | 200 | 8.10312 | {u'status': 2, u'maxcv': 0.0, u'success': Fals... | ||||||
10 | 10 | 4ffebda70760cefc | 0 | -2.80778 | -2.73778 | -2.80778 | {u'maxiter': 10} | def my_cobyla( func, x0, my_args=(), my_option... | 10 | -1 | {u'total_q_shots': 400, u'total_q_seconds': 8.... | {u'vqe_output': {u'status': 2, u'maxcv': 0.0, ... | False | 8.55817 | 400 | 8.62367 | {u'status': 2, u'maxcv': 0.0, u'success': Fals... | |||
1 | -2.80778 | -2.57372 | -2.52701 | {u'maxiter': 10} | def my_cobyla( func, x0, my_args=(), my_option... | 10 | -1 | {u'total_q_shots': 400, u'total_q_seconds': 9.... | {u'vqe_output': {u'status': 2, u'maxcv': 0.0, ... | False | 9.53145 | 400 | 9.57675 | {u'status': 2, u'maxcv': 0.0, u'success': Fals... | ||||||
2 | -2.80778 | -2.79475 | -2.80778 | {u'maxiter': 10} | def my_cobyla( func, x0, my_args=(), my_option... | 10 | -1 | {u'total_q_shots': 400, u'total_q_seconds': 9.... | {u'vqe_output': {u'status': 2, u'maxcv': 0.0, ... | False | 9.97107 | 400 | 10.0142 | {u'status': 2, u'maxcv': 0.0, u'success': Fals... | ||||||
50 | 1 | c6e3ed24f7842f6a | 0 | -1.0108 | -0.819667 | -0.954647 | {u'maxiter': 1} | def my_cobyla( func, x0, my_args=(), my_option... | 1 | -1 | {u'total_q_shots': 200, u'total_q_seconds': 0.... | {u'vqe_output': {u'status': 2, u'maxcv': 0.0, ... | False | 0.790883 | 200 | 0.806937 | {u'status': 2, u'maxcv': 0.0, u'success': Fals... | |||
1 | -0.758102 | -0.819667 | -0.842335 | {u'maxiter': 1} | def my_cobyla( func, x0, my_args=(), my_option... | 1 | -1 | {u'total_q_shots': 200, u'total_q_seconds': 0.... | {u'vqe_output': {u'status': 2, u'maxcv': 0.0, ... | False | 0.806394 | 200 | 0.817754 | {u'status': 2, u'maxcv': 0.0, u'success': Fals... | ||||||
2 | -0.898491 | -0.819667 | -0.954647 | {u'maxiter': 1} | def my_cobyla( func, x0, my_args=(), my_option... | 1 | -1 | {u'total_q_shots': 200, u'total_q_seconds': 0.... | {u'vqe_output': {u'status': 2, u'maxcv': 0.0, ... | False | 0.838187 | 200 | 0.866396 | {u'status': 2, u'maxcv': 0.0, u'success': Fals... | ||||||
30 | 3c1a71fb284ef7a3 | 0 | -2.80778 | -2.80149 | -2.80778 | {u'maxiter': 30} | def my_cobyla( func, x0, my_args=(), my_option... | 21 | -1 | {u'total_q_shots': 4200, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 26.6657 | 4200 | 26.9283 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||
1 | -2.77971 | -2.8039 | -2.80778 | {u'maxiter': 30} | def my_cobyla( func, x0, my_args=(), my_option... | 24 | -1 | {u'total_q_shots': 4800, u'total_q_seconds': 3... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 33.8779 | 4800 | 34.1974 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
2 | -2.80778 | -2.79729 | -2.80778 | {u'maxiter': 30} | def my_cobyla( func, x0, my_args=(), my_option... | 21 | -1 | {u'total_q_shots': 4200, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 25.4113 | 4200 | 25.7054 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
3 | -2.80778 | -2.8063 | -2.80778 | {u'maxiter': 30} | def my_cobyla( func, x0, my_args=(), my_option... | 18 | -1 | {u'total_q_shots': 3600, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 21.3779 | 3600 | 21.6107 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
4 | -2.80778 | -2.80535 | -2.80778 | {u'maxiter': 30} | def my_cobyla( func, x0, my_args=(), my_option... | 18 | -1 | {u'total_q_shots': 3600, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 22.5721 | 3600 | 22.7178 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
5 | -2.80778 | -2.80075 | -2.77971 | {u'maxiter': 30} | def my_cobyla( func, x0, my_args=(), my_option... | 20 | -1 | {u'total_q_shots': 4000, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 22.9206 | 4000 | 23.1588 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
6 | -2.80778 | -2.80656 | -2.80778 | {u'maxiter': 30} | def my_cobyla( func, x0, my_args=(), my_option... | 22 | -1 | {u'total_q_shots': 4400, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 26.5498 | 4400 | 26.8271 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
7 | -2.77971 | -2.76775 | -2.75163 | {u'maxiter': 30} | def my_cobyla( func, x0, my_args=(), my_option... | 29 | -1 | {u'total_q_shots': 5800, u'total_q_seconds': 3... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 30.5005 | 5800 | 30.8733 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
8 | -2.80778 | -2.76296 | -2.75163 | {u'maxiter': 30} | def my_cobyla( func, x0, my_args=(), my_option... | 25 | -1 | {u'total_q_shots': 5000, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 26.488 | 5000 | 26.8561 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
100 | 80 | bb013002e7edb3a3 | 0 | -2.80778 | -2.799 | -2.80778 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 22 | -1 | {u'total_q_shots': 8800, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 26.2054 | 8800 | 26.4186 | {u'status': 1, u'maxcv': 0.0, u'success': True... | |||
1 | -2.80778 | -2.80063 | -2.80778 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 24 | -1 | {u'total_q_shots': 9600, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 26.8684 | 9600 | 27.1461 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
2 | -2.80778 | -2.78071 | -2.77971 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 31 | -1 | {u'total_q_shots': 12400, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 32.1505 | 12400 | 32.4813 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
my_minimizer | 5 | 10 | 2b74910afe36e6b5 | 0 | -1.68467 | -1.97706 | -1.68467 | {u'maxfev': 10} | def my_minimizer( func, x0, my_args=(), my_opt... | 15 | 1 | {u'total_q_shots': 300, u'total_q_seconds': 16... | {u'vqe_output': {u'status': 0, u'success': Tru... | True | 16.596 | 300 | 16.6778 | {u'status': 0, u'success': True, u'fun_validat... | ||
1 | -2.80778 | -2.80778 | -2.80778 | {u'maxfev': 10} | def my_minimizer( func, x0, my_args=(), my_opt... | 26 | 4 | {u'total_q_shots': 520, u'total_q_seconds': 27... | {u'vqe_output': {u'status': 0, u'success': Tru... | True | 27.9888 | 520 | 28.114 | {u'status': 0, u'success': True, u'fun_validat... | ||||||
2 | -1.96545 | -1.97706 | -1.12311 | {u'maxfev': 10} | def my_minimizer( func, x0, my_args=(), my_opt... | 18 | 2 | {u'total_q_shots': 360, u'total_q_seconds': 20... | {u'vqe_output': {u'status': 4, u'success': Fal... | False | 20.7148 | 360 | 20.7955 | {u'status': 4, u'success': False, u'fun_valida... | ||||||
9f9bf704d869c823 | 0 | -2.80778 | -2.80726 | -2.80778 | {u'maxfev': 10} | def my_minimizer( func, x0, my_args=(), my_opt... | 88 | 7 | {u'total_q_shots': 1760, u'total_q_seconds': 8... | {u'vqe_output': {u'status': 0, u'success': Tru... | True | 82.9774 | 1760 | 83.4299 | {u'status': 0, u'success': True, u'fun_validat... | |||||
1 | -0.280778 | -0.0635588 | 1.11022e-16 | {u'maxfev': 10} | def my_minimizer( func, x0, my_args=(), my_opt... | 32 | 3 | {u'total_q_shots': 640, u'total_q_seconds': 37... | {u'vqe_output': {u'status': 0, u'success': Tru... | True | 37.5214 | 640 | 37.6667 | {u'status': 0, u'success': True, u'fun_validat... | ||||||
2 | 1.11022e-16 | -0.126606 | 1.11022e-16 | {u'maxfev': 10} | def my_minimizer( func, x0, my_args=(), my_opt... | 18 | 2 | {u'total_q_shots': 360, u'total_q_seconds': 18... | {u'vqe_output': {u'status': 0, u'success': Tru... | True | 18.7542 | 360 | 18.8307 | {u'status': 0, u'success': True, u'fun_validat... | ||||||
b7125fab439a9e3a | 0 | -1.12311 | -0.819667 | -1.12311 | {u'maxfev': 10} | def my_minimizer( func, x0, my_args=(), my_opt... | 4 | 1 | {u'total_q_shots': 80, u'total_q_seconds': 5.7... | {u'vqe_output': {u'status': 0, u'success': Tru... | True | 5.75664 | 80 | 5.77586 | {u'status': 0, u'success': True, u'fun_validat... | |||||
1 | 1.11022e-16 | -0.0872042 | 1.11022e-16 | {u'maxfev': 10} | def my_minimizer( func, x0, my_args=(), my_opt... | 18 | 2 | {u'total_q_shots': 360, u'total_q_seconds': 21... | {u'vqe_output': {u'status': 0, u'success': Tru... | True | 21.9616 | 360 | 22.054 | {u'status': 0, u'success': True, u'fun_validat... | ||||||
2 | -0.561557 | -1.14825 | -1.12311 | {u'maxfev': 10} | def my_minimizer( func, x0, my_args=(), my_opt... | 32 | 3 | {u'total_q_shots': 640, u'total_q_seconds': 35... | {u'vqe_output': {u'status': 0, u'success': Tru... | True | 35.7381 | 640 | 35.8776 | {u'status': 0, u'success': True, u'fun_validat... | ||||||
10 | 30 | 314e14a49a71437e | 0 | -2.80778 | -2.80777 | -2.80778 | {u'maxfev': 30} | def my_minimizer( func, x0, my_args=(), my_opt... | 56 | 8 | {u'total_q_shots': 2240, u'total_q_seconds': 5... | {u'vqe_output': {u'status': 0, u'success': Tru... | True | 57.7906 | 2240 | 58.118 | {u'status': 0, u'success': True, u'fun_validat... | |||
1 | -2.80778 | -2.6927 | -2.52701 | {u'maxfev': 30} | def my_minimizer( func, x0, my_args=(), my_opt... | 50 | 9 | {u'total_q_shots': 2000, u'total_q_seconds': 5... | {u'vqe_output': {u'status': 8, u'success': Fal... | False | 52.2629 | 2000 | 52.5637 | {u'status': 8, u'success': False, u'fun_valida... | ||||||
2 | -2.80778 | -2.78666 | -2.80778 | {u'maxfev': 30} | def my_minimizer( func, x0, my_args=(), my_opt... | 18 | -1 | {u'total_q_shots': 720, u'total_q_seconds': 19... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 19.5183 | 720 | 19.6038 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
50 | 30 | 491b2d69510a4ba8 | 0 | -0.870413 | -0.819667 | -0.730024 | {u'maxfev': 30} | def my_minimizer( func, x0, my_args=(), my_opt... | 1 | 1 | {u'total_q_shots': 200, u'total_q_seconds': 1.... | {u'vqe_output': {u'fun_validated': -0.73002382... | False | 1.79757 | 200 | 1.80848 | {u'fun_validated': -0.73002382896, u'nfev': 1,... | |||
1 | -0.814257 | -0.819667 | -0.870413 | {u'maxfev': 30} | def my_minimizer( func, x0, my_args=(), my_opt... | 1 | 1 | {u'total_q_shots': 200, u'total_q_seconds': 1.... | {u'vqe_output': {u'fun_validated': -0.87041302... | False | 1.4813 | 200 | 1.49949 | {u'fun_validated': -0.870413026837, u'nfev': 1... | ||||||
2 | -0.64579 | -0.819667 | -0.898491 | {u'maxfev': 30} | def my_minimizer( func, x0, my_args=(), my_opt... | 1 | 1 | {u'total_q_shots': 200, u'total_q_seconds': 1.... | {u'vqe_output': {u'fun_validated': -0.89849086... | False | 1.9314 | 200 | 1.94654 | {u'fun_validated': -0.898490866413, u'nfev': 1... | ||||||
3 | -0.78618 | -0.819667 | -0.730024 | {u'maxfev': 30} | def my_minimizer( func, x0, my_args=(), my_opt... | 1 | 1 | {u'total_q_shots': 200, u'total_q_seconds': 1.... | {u'vqe_output': {u'fun_validated': -0.73002382... | False | 1.1248 | 200 | 1.14278 | {u'fun_validated': -0.73002382896, u'nfev': 1,... | ||||||
4 | -0.64579 | -0.819667 | -0.64579 | {u'maxfev': 30} | def my_minimizer( func, x0, my_args=(), my_opt... | 1 | 1 | {u'total_q_shots': 200, u'total_q_seconds': 0.... | {u'vqe_output': {u'fun_validated': -0.64579031... | False | 0.777182 | 200 | 0.781641 | {u'fun_validated': -0.645790310234, u'nfev': 1... | ||||||
5 | -0.870413 | -0.819667 | -0.78618 | {u'maxfev': 30} | def my_minimizer( func, x0, my_args=(), my_opt... | 1 | 1 | {u'total_q_shots': 200, u'total_q_seconds': 1.... | {u'vqe_output': {u'fun_validated': -0.78617950... | False | 1.07168 | 200 | 1.07577 | {u'fun_validated': -0.786179508111, u'nfev': 1... | ||||||
6 | -0.954647 | -0.819667 | -0.730024 | {u'maxfev': 30} | def my_minimizer( func, x0, my_args=(), my_opt... | 1 | 1 | {u'total_q_shots': 200, u'total_q_seconds': 1.... | {u'vqe_output': {u'fun_validated': -0.73002382... | False | 1.1323 | 200 | 1.15332 | {u'fun_validated': -0.73002382896, u'nfev': 1,... | ||||||
7 | -0.842335 | -0.819667 | -0.673868 | {u'maxfev': 30} | def my_minimizer( func, x0, my_args=(), my_opt... | 1 | 1 | {u'total_q_shots': 200, u'total_q_seconds': 0.... | {u'vqe_output': {u'fun_validated': -0.67386814... | False | 0.804977 | 200 | 0.823108 | {u'fun_validated': -0.67386814981, u'nfev': 1,... | ||||||
8 | -0.814257 | -0.819667 | -0.78618 | {u'maxfev': 30} | def my_minimizer( func, x0, my_args=(), my_opt... | 1 | 1 | {u'total_q_shots': 200, u'total_q_seconds': 1.... | {u'vqe_output': {u'fun_validated': -0.78617950... | False | 1.86342 | 200 | 1.87722 | {u'fun_validated': -0.786179508111, u'nfev': 1... | ||||||
team-04 | my_nelder_mead | 160 | 80 | 925f197b594663a6 | 0 | -1.38634 | -1.17955 | -1.28983 | {u'maxfev': 80} | def my_nelder_mead( func, x0, my_args=(), my_o... | 80 | 36 | {u'total_q_shots': 51200, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 69.5198 | 51200 | 71.469 | {u'status': 1, u'success': False, u'final_simp... | |
1 | -1.03537 | -0.82124 | -0.763366 | {u'maxfev': 80} | def my_nelder_mead( func, x0, my_args=(), my_o... | 80 | 32 | {u'total_q_shots': 51200, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 75.7881 | 51200 | 77.7511 | {u'status': 1, u'success': False, u'final_simp... | ||||||
2 | -0.982724 | -0.819667 | -0.85111 | {u'maxfev': 80} | def my_nelder_mead( func, x0, my_args=(), my_o... | 80 | 29 | {u'total_q_shots': 51200, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 70.6511 | 51200 | 72.3971 | {u'status': 1, u'success': False, u'final_simp... | ||||||
team-05 | my_nelder_mead | 80 | 50 | c5e24abc476f8367 | 0 | -1.22841 | -0.827657 | -0.631751 | {u'maxfev': 50} | def my_nelder_mead( func, x0, my_args=(), my_o... | 50 | 20 | {u'total_q_shots': 16000, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 52.5013 | 16000 | 53.1516 | {u'status': 1, u'success': False, u'final_simp... | |
1 | -0.982724 | -0.788031 | -0.701946 | {u'maxfev': 50} | def my_nelder_mead( func, x0, my_args=(), my_o... | 50 | 18 | {u'total_q_shots': 16000, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 52.2987 | 16000 | 52.9763 | {u'status': 1, u'success': False, u'final_simp... | ||||||
2 | -1.12311 | -0.851614 | -1.05292 | {u'maxfev': 50} | def my_nelder_mead( func, x0, my_args=(), my_o... | 51 | 19 | {u'total_q_shots': 16320, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 50.0949 | 16320 | 50.7293 | {u'status': 1, u'success': False, u'final_simp... | ||||||
team-06 | my_cobyla | 75 | 80 | 393eeec44fb694bc | 0 | -2.80778 | -2.80532 | -2.80778 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 20 | -1 | {u'total_q_shots': 6000, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 27.2538 | 6000 | 27.3329 | {u'status': 1, u'maxcv': 0.0, u'success': True... | |
1 | -2.80778 | -2.80372 | -2.80778 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 19 | -1 | {u'total_q_shots': 5700, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 22.2383 | 5700 | 22.3067 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
2 | -2.80778 | -2.80739 | -2.80778 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 20 | -1 | {u'total_q_shots': 6000, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 21.0262 | 6000 | 21.0946 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
100 | 80 | 7229f2ac1a4f408f | 0 | -2.80778 | -2.80778 | -2.80778 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 20 | -1 | {u'total_q_shots': 8000, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 26.2084 | 8000 | 26.3085 | {u'status': 1, u'maxcv': 0.0, u'success': True... | |||
1 | -2.80778 | -2.80752 | -2.80778 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 20 | -1 | {u'total_q_shots': 8000, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 27.249 | 8000 | 27.3427 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
2 | -2.80778 | -2.80743 | -2.80778 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 18 | -1 | {u'total_q_shots': 7200, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 24.2884 | 7200 | 24.3644 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
3 | -2.80778 | -2.80778 | -2.80778 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 19 | -1 | {u'total_q_shots': 7600, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 22.027 | 7600 | 22.1123 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
4 | -2.80778 | -2.79962 | -2.80778 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 22 | -1 | {u'total_q_shots': 8800, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 28.0663 | 8800 | 28.1629 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
5 | -2.80778 | -2.80778 | -2.80778 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 19 | -1 | {u'total_q_shots': 7600, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 26.1289 | 7600 | 26.2133 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
team-07 | my_cobyla | 150 | 80 | 114a1dbc72e659cb | 0 | -2.80778 | -2.80665 | -2.80778 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 18 | -1 | {u'total_q_shots': 10800, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 19.4348 | 10800 | 19.787 | {u'status': 1, u'maxcv': 0.0, u'success': True... | |
1 | -2.80778 | -2.80622 | -2.80778 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 26 | -1 | {u'total_q_shots': 15600, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 26.3869 | 15600 | 26.6828 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
2 | -2.80778 | -2.807 | -2.80778 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 19 | -1 | {u'total_q_shots': 11400, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 22.8704 | 11400 | 23.1726 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
my_conjuage | 50 | -1 | a0c1334b598d1d50 | 0 | -1.0108 | -0.819745 | -0.673868 | {} | def my_conjuage( func, x0, my_args=(), my_opti... | 120 | 2 | {u'total_q_shots': 24000, u'total_q_seconds': ... | {u'vqe_output': {u'status': 2, u'success': Fal... | False | 122.091 | 24000 | 122.586 | {u'status': 2, u'success': False, u'fun_valida... | ||
1 | -1.03888 | -0.819666 | -0.561557 | {} | def my_conjuage( func, x0, my_args=(), my_opti... | 107 | 1 | {u'total_q_shots': 21400, u'total_q_seconds': ... | {u'vqe_output': {u'status': 2, u'success': Fal... | False | 111.936 | 21400 | 112.374 | {u'status': 2, u'success': False, u'fun_valida... | ||||||
2 | -1.12311 | -0.819635 | -0.814257 | {} | def my_conjuage( func, x0, my_args=(), my_opti... | 84 | 1 | {u'total_q_shots': 16800, u'total_q_seconds': ... | {u'vqe_output': {u'status': 2, u'success': Fal... | False | 72.9258 | 16800 | 73.2406 | {u'status': 2, u'success': False, u'fun_valida... | ||||||
my_nelder_mead | 150 | 80 | 9629cdd4afb92cba | 0 | -2.80778 | -2.80416 | -2.80778 | {u'maxfev': 80} | def my_nelder_mead( func, x0, my_args=(), my_o... | 73 | 27 | {u'total_q_shots': 43800, u'total_q_seconds': ... | {u'vqe_output': {u'status': 0, u'success': Tru... | True | 67.1307 | 43800 | 68.3154 | {u'status': 0, u'success': True, u'final_simpl... | ||
1 | -1.08568 | -0.91714 | -0.954647 | {u'maxfev': 80} | def my_nelder_mead( func, x0, my_args=(), my_o... | 83 | 31 | {u'total_q_shots': 49800, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 73.8707 | 49800 | 74.6631 | {u'status': 1, u'success': False, u'final_simp... | ||||||
2 | -1.0108 | -0.756597 | -0.879772 | {u'maxfev': 80} | def my_nelder_mead( func, x0, my_args=(), my_o... | 83 | 33 | {u'total_q_shots': 49800, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 72.3375 | 49800 | 73.176 | {u'status': 1, u'success': False, u'final_simp... | ||||||
team-08 | my_nelder_mead | 40 | 80 | 1f2618007bf47498 | 0 | -1.19331 | -0.756565 | -0.807238 | {u'maxfev': 80} | def my_nelder_mead( func, x0, my_args=(), my_o... | 83 | 32 | {u'total_q_shots': 13280, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 74.766 | 13280 | 75.2298 | {u'status': 1, u'success': False, u'final_simp... | |
1 | -1.40389 | -0.818282 | -0.877432 | {u'maxfev': 80} | def my_nelder_mead( func, x0, my_args=(), my_o... | 81 | 32 | {u'total_q_shots': 12960, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 72.9845 | 12960 | 73.441 | {u'status': 1, u'success': False, u'final_simp... | ||||||
2 | -1.47409 | -0.819643 | -1.19331 | {u'maxfev': 80} | def my_nelder_mead( func, x0, my_args=(), my_o... | 81 | 31 | {u'total_q_shots': 12960, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 67.2592 | 12960 | 67.6866 | {u'status': 1, u'success': False, u'final_simp... | ||||||
100 | 80 | ec34936968a6ded1 | 0 | -0.968685 | -0.729564 | -0.870413 | {u'maxfev': 80} | def my_nelder_mead( func, x0, my_args=(), my_o... | 80 | 34 | {u'total_q_shots': 32000, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 73.5952 | 32000 | 74.5244 | {u'status': 1, u'success': False, u'final_simp... | |||
1 | -2.80778 | -2.80777 | -2.80778 | {u'maxfev': 80} | def my_nelder_mead( func, x0, my_args=(), my_o... | 68 | 24 | {u'total_q_shots': 27200, u'total_q_seconds': ... | {u'vqe_output': {u'status': 0, u'success': Tru... | True | 64.1848 | 27200 | 64.8795 | {u'status': 0, u'success': True, u'final_simpl... | ||||||
2 | -1.13715 | -0.869965 | -0.982724 | {u'maxfev': 80} | def my_nelder_mead( func, x0, my_args=(), my_o... | 80 | 32 | {u'total_q_shots': 32000, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 69.9478 | 32000 | 70.8059 | {u'status': 1, u'success': False, u'final_simp... | ||||||
team-09 | my_cobyla | 2 | 80 | 250b2eb6b7477af5 | 0 | -2.80778 | -2.57363 | -2.10584 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 16 | -1 | {u'total_q_shots': 128, u'total_q_seconds': 17... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 17.0106 | 128 | 17.0467 | {u'status': 1, u'maxcv': 0.0, u'success': True... | |
1 | -2.80778 | -2.57372 | -2.80778 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 19 | -1 | {u'total_q_shots': 152, u'total_q_seconds': 19... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 19.5809 | 152 | 19.623 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
2 | -1.40389 | -0.783735 | -0.701946 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 29 | -1 | {u'total_q_shots': 232, u'total_q_seconds': 27... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 27.4433 | 232 | 27.5099 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
10 | 80 | a95e224f9d46c9bc | 0 | -2.52701 | -2.57371 | -2.52701 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 28 | -1 | {u'total_q_shots': 1120, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 27.7537 | 1120 | 27.8511 | {u'status': 1, u'maxcv': 0.0, u'success': True... | |||
1 | -2.80778 | -2.55194 | -2.80778 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 30 | -1 | {u'total_q_shots': 1200, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 29.581 | 1200 | 29.6844 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
2 | -2.80778 | -2.80769 | -2.80778 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 20 | -1 | {u'total_q_shots': 800, u'total_q_seconds': 19... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 19.5378 | 800 | 19.6086 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
16 | 80 | ea30144a65b56ed1 | 0 | -2.28132 | -2.57851 | -2.45681 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 29 | -1 | {u'total_q_shots': 1856, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 26.4714 | 1856 | 26.5941 | {u'status': 1, u'maxcv': 0.0, u'success': True... | |||
1 | -2.80778 | -2.76142 | -2.80778 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 25 | -1 | {u'total_q_shots': 1600, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 24.645 | 1600 | 24.7498 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
2 | -2.80778 | -2.76463 | -2.72004 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 25 | -1 | {u'total_q_shots': 1600, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 22.9286 | 1600 | 23.0334 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
18 | 80 | 629ceefcd1714629 | 0 | -2.80778 | -2.71476 | -2.80778 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 28 | -1 | {u'total_q_shots': 2016, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 27.1409 | 2016 | 27.2655 | {u'status': 1, u'maxcv': 0.0, u'success': True... | |||
1 | -2.80778 | -2.79107 | -2.80778 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 20 | -1 | {u'total_q_shots': 1440, u'total_q_seconds': 1... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 19.1101 | 1440 | 19.1999 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
2 | -2.80778 | -2.79924 | -2.80778 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 18 | -1 | {u'total_q_shots': 1296, u'total_q_seconds': 1... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 17.3081 | 1296 | 17.3906 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
20 | 80 | d44f28297a82c9e2 | 0 | -2.45681 | -2.57381 | -2.45681 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 28 | -1 | {u'total_q_shots': 2240, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 28.6099 | 2240 | 28.745 | {u'status': 1, u'maxcv': 0.0, u'success': True... | |||
1 | -2.80778 | -2.80119 | -2.80778 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 21 | -1 | {u'total_q_shots': 1680, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 23.2621 | 1680 | 23.3614 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
2 | -2.80778 | -2.74889 | -2.80778 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 27 | -1 | {u'total_q_shots': 2160, u'total_q_seconds': 3... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 31.0118 | 2160 | 31.1374 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
40 | 80 | ab01a50c47a6ac7e | 0 | -2.80778 | -2.77124 | -2.77269 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 29 | -1 | {u'total_q_shots': 4640, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 27.9407 | 4640 | 28.161 | {u'status': 1, u'maxcv': 0.0, u'success': True... | |||
1 | -2.80778 | -2.79767 | -2.80778 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 21 | -1 | {u'total_q_shots': 3360, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 20.8228 | 3360 | 20.9793 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
2 | -2.80778 | -2.80763 | -2.80778 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 18 | -1 | {u'total_q_shots': 2880, u'total_q_seconds': 1... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 17.1688 | 2880 | 17.3151 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
100 | 80 | 8549d1ba56d120a4 | 0 | -2.56912 | -2.66354 | -2.66739 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 27 | -1 | {u'total_q_shots': 10800, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 27.0391 | 10800 | 27.4852 | {u'status': 1, u'maxcv': 0.0, u'success': True... | |||
1 | -2.79375 | -2.78543 | -2.77971 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 27 | -1 | {u'total_q_shots': 10800, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 26.7986 | 10800 | 27.2369 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
2 | -2.80778 | -2.80149 | -2.79375 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 23 | -1 | {u'total_q_shots': 9200, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 22.3481 | 9200 | 22.718 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
140 | 80 | 46f4687bd0fc29c9 | 0 | -2.80778 | -2.80776 | -2.80778 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 19 | -1 | {u'total_q_shots': 10640, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 22.0644 | 10640 | 22.4644 | {u'status': 1, u'maxcv': 0.0, u'success': True... | |||
1 | -2.80778 | -2.80774 | -2.80778 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 18 | -1 | {u'total_q_shots': 10080, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 20.4993 | 10080 | 20.8785 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
2 | -2.80778 | -2.80778 | -2.80778 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 20 | -1 | {u'total_q_shots': 11200, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 24.1922 | 11200 | 24.6192 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
200 | 80 | 045e5aad57c12b50 | 0 | -2.80778 | -2.80775 | -2.80778 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 20 | -1 | {u'total_q_shots': 16000, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 22.1557 | 16000 | 22.7582 | {u'status': 1, u'maxcv': 0.0, u'success': True... | |||
1 | -2.80778 | -2.80471 | -2.80778 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 21 | -1 | {u'total_q_shots': 16800, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 21.4448 | 16800 | 22.0617 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
2 | -2.80778 | -2.80503 | -2.80778 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 27 | -1 | {u'total_q_shots': 21600, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 28.415 | 21600 | 29.2147 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
team-10 | my_cobyla | 100 | 80 | 78fc1d13c58f82a3 | 0 | -2.80778 | -2.77965 | -2.77971 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 30 | -1 | {u'total_q_shots': 12000, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 40.5029 | 12000 | 40.6952 | {u'status': 1, u'maxcv': 0.0, u'success': True... | |
1 | -2.80778 | -2.79886 | -2.80778 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 22 | -1 | {u'total_q_shots': 8800, u'total_q_seconds': 3... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 32.7359 | 8800 | 32.8702 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
2 | -2.80778 | -2.80257 | -2.80778 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 19 | -1 | {u'total_q_shots': 7600, u'total_q_seconds': 3... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 31.5266 | 7600 | 31.6437 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
team-11 | my_cobyla | 100 | 80 | 35ba631d4fd7a6fd | 0 | -2.80778 | -2.80681 | -2.80778 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 19 | -1 | {u'total_q_shots': 7600, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 23.7401 | 7600 | 23.9625 | {u'status': 1, u'maxcv': 0.0, u'success': True... | |
1 | -2.80778 | -2.80687 | -2.80778 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 22 | -1 | {u'total_q_shots': 8800, u'total_q_seconds': 3... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 30.7428 | 8800 | 30.989 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
2 | -2.80778 | -2.80433 | -2.80778 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 21 | -1 | {u'total_q_shots': 8400, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 27.8212 | 8400 | 28.057 | {u'status': 1, u'maxcv': 0.0, u'success': True... | ||||||
team-12 | my_cobyla | 1 | 1 | eade07d3047bdb24 | 0 | 1.11022e-16 | -0.819667 | 1.11022e-16 | {u'maxiter': 1} | def my_cobyla( func, x0, my_args=(), my_option... | 1 | -1 | {u'total_q_shots': 4, u'total_q_seconds': 1.06... | {u'vqe_output': {u'status': 2, u'maxcv': 0.0, ... | False | 1.06729 | 4 | 1.06891 | {u'status': 2, u'maxcv': 0.0, u'success': Fals... | |
1 | 1.11022e-16 | -0.819667 | 1.11022e-16 | {u'maxiter': 1} | def my_cobyla( func, x0, my_args=(), my_option... | 1 | -1 | {u'total_q_shots': 4, u'total_q_seconds': 0.72... | {u'vqe_output': {u'status': 2, u'maxcv': 0.0, ... | False | 0.726941 | 4 | 0.728611 | {u'status': 2, u'maxcv': 0.0, u'success': Fals... | ||||||
2 | 1.11022e-16 | -0.819667 | 1.11022e-16 | {u'maxiter': 1} | def my_cobyla( func, x0, my_args=(), my_option... | 1 | -1 | {u'total_q_shots': 4, u'total_q_seconds': 1.04... | {u'vqe_output': {u'status': 2, u'maxcv': 0.0, ... | False | 1.04924 | 4 | 1.0507 | {u'status': 2, u'maxcv': 0.0, u'success': Fals... | ||||||
my_minimizer | 1 | 1 | 019aa2420742c0a3 | 0 | 1.11022e-16 | -0.66215 | -1.40389 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 15 | 1 | {u'total_q_shots': 8, u'total_q_seconds': 1.82... | {u'vqe_output': {u'fun_validated': -1.40389197... | False | 1.82707 | 8 | 1.90709 | {u'fun_validated': -1.40389197877, u'nfev': 15... | ||
1f72b8e0f40fdb44 | 0 | -2.80778 | -2.71276 | -2.80778 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 70 | 1 | {u'total_q_shots': 284, u'total_q_seconds': 74... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 74.4332 | 284 | 116.381 | {u'fun_validated': -2.80778395754, u'nfev': 70... | |||||
303840188a953656 | 0 | -2.80778 | -1.42066 | -2.80778 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 15 | 1 | {u'total_q_shots': 56, u'total_q_seconds': 16.... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 16.3796 | 56 | 19.5659 | {u'fun_validated': -2.80778395754, u'nfev': 15... | |||||
3d09e495e79945dd | 0 | 1.11022e-16 | -1.20062 | 1.11022e-16 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 15 | 1 | {u'total_q_shots': 56, u'total_q_seconds': 14.... | {u'vqe_output': {u'fun_validated': 1.110223024... | False | 14.8522 | 56 | 18.717 | {u'fun_validated': 1.11022302463e-16, u'nfev':... | |||||
5c2e4aa0abefcc7b | 0 | -2.80778 | -2.57174 | -2.80778 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 65 | 1 | {u'total_q_shots': 264, u'total_q_seconds': 75... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 75.8066 | 264 | 176.286 | {u'fun_validated': -2.80778395754, u'nfev': 65... | |||||
1 | -1.40389 | -1.15442 | 1.11022e-16 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 65 | 1 | {u'total_q_shots': 264, u'total_q_seconds': 68... | {u'vqe_output': {u'fun_validated': 1.110223024... | False | 68.4896 | 264 | 207.697 | {u'fun_validated': 1.11022302463e-16, u'nfev':... | ||||||
2 | 1.11022e-16 | -2.50164 | -1.40389 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 65 | 1 | {u'total_q_shots': 264, u'total_q_seconds': 66... | {u'vqe_output': {u'fun_validated': -1.40389197... | False | 66.3466 | 264 | 149.526 | {u'fun_validated': -1.40389197877, u'nfev': 65... | ||||||
3 | -1.40389 | -1.96753 | -1.40389 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 65 | 1 | {u'total_q_shots': 264, u'total_q_seconds': 66... | {u'vqe_output': {u'fun_validated': -1.40389197... | False | 66.2451 | 264 | 136.596 | {u'fun_validated': -1.40389197877, u'nfev': 65... | ||||||
4 | 1.11022e-16 | -1.60556 | -1.40389 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 65 | 1 | {u'total_q_shots': 264, u'total_q_seconds': 65... | {u'vqe_output': {u'fun_validated': -1.40389197... | False | 65.7897 | 264 | 96.3545 | {u'fun_validated': -1.40389197877, u'nfev': 65... | ||||||
5 | -2.80778 | -2.65638 | -2.80778 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 65 | 1 | {u'total_q_shots': 264, u'total_q_seconds': 71... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 71.787 | 264 | 140.64 | {u'fun_validated': -2.80778395754, u'nfev': 65... | ||||||
6 | -2.80778 | -1.87078 | -2.80778 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 65 | 1 | {u'total_q_shots': 264, u'total_q_seconds': 68... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 68.7745 | 264 | 115.927 | {u'fun_validated': -2.80778395754, u'nfev': 65... | ||||||
7 | -1.40389 | -1.56135 | -2.80778 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 65 | 1 | {u'total_q_shots': 264, u'total_q_seconds': 76... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 76.6993 | 264 | 123.179 | {u'fun_validated': -2.80778395754, u'nfev': 65... | ||||||
8 | -2.80778 | -2.14874 | -2.80778 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 65 | 1 | {u'total_q_shots': 264, u'total_q_seconds': 77... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 77.481 | 264 | 116.792 | {u'fun_validated': -2.80778395754, u'nfev': 65... | ||||||
9 | -1.40389 | -2.1934 | 1.11022e-16 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 65 | 1 | {u'total_q_shots': 264, u'total_q_seconds': 83... | {u'vqe_output': {u'fun_validated': 1.110223024... | False | 83.9451 | 264 | 136.22 | {u'fun_validated': 1.11022302463e-16, u'nfev':... | ||||||
5f7608e149258788 | 0 | -1.40389 | -2.63267 | -1.40389 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 15 | 1 | {u'total_q_shots': 8, u'total_q_seconds': 1.85... | {u'vqe_output': {u'fun_validated': -1.40389197... | False | 1.8528 | 8 | 1.98644 | {u'fun_validated': -1.40389197877, u'nfev': 15... | |||||
63942deb93ed4924 | 0 | -2.80778 | -2.57268 | -2.80778 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 65 | 1 | {u'total_q_shots': 264, u'total_q_seconds': 90... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 90.9515 | 264 | 231.66 | {u'fun_validated': -2.80778395754, u'nfev': 65... | |||||
79d974140d498e28 | 0 | 1.11022e-16 | -0.335635 | 1.11022e-16 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 15 | 1 | {u'total_q_shots': 8, u'total_q_seconds': 3.80... | {u'vqe_output': {u'fun_validated': 1.110223024... | False | 3.80858 | 8 | 3.89904 | {u'fun_validated': 1.11022302463e-16, u'nfev':... | |||||
1 | 1.11022e-16 | -0.109345 | 1.11022e-16 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 15 | 1 | {u'total_q_shots': 8, u'total_q_seconds': 1.87... | {u'vqe_output': {u'fun_validated': 1.110223024... | False | 1.87855 | 8 | 1.96968 | {u'fun_validated': 1.11022302463e-16, u'nfev':... | ||||||
999f11859d44c8c9 | 0 | [3.4886765408761726, 1.5156803717638714] | -0.819667 | -1.40389 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 15 | 1 | {u'total_q_shots': 64, u'total_q_seconds': 19.... | {u'vqe_output': {u'fun_validated': -1.40389197... | False | 19.1474 | 64 | 24.6217 | {u'fun_validated': -1.40389197877, u'nfev': 15... | |||||
1 | [5.070733890728206, 2.1975801009657125] | -0.819667 | 1.11022e-16 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 15 | 1 | {u'total_q_shots': 64, u'total_q_seconds': 19.... | {u'vqe_output': {u'fun_validated': 1.110223024... | False | 19.2697 | 64 | 23.2182 | {u'fun_validated': 1.11022302463e-16, u'nfev':... | ||||||
2 | [0.9961193939582954, 4.985770376291675] | -0.819667 | -2.80778 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 15 | 1 | {u'total_q_shots': 64, u'total_q_seconds': 19.... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 19.3407 | 64 | 23.4106 | {u'fun_validated': -2.80778395754, u'nfev': 15... | ||||||
c24948fb82b1b9b2 | 0 | 1.11022e-16 | -1.22427 | -1.40389 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 15 | 1 | {u'total_q_shots': 56, u'total_q_seconds': 15.... | {u'vqe_output': {u'fun_validated': -1.40389197... | False | 15.0714 | 56 | 193.411 | {u'fun_validated': -1.40389197877, u'nfev': 15... | |||||
5 | 1 | 2d241af9ac061a2b | 0 | -2.80778 | -2.79836 | -2.80778 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 200 | 1 | {u'total_q_shots': 820, u'total_q_seconds': 46... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 46.0737 | 820 | 72.8218 | {u'fun_validated': -2.80778395754, u'nfev': 20... | |||
10 | 1 | 334bbebf811298b1 | 0 | -2.80778 | -2.61727 | -2.38662 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 15 | 1 | {u'total_q_shots': 1640, u'total_q_seconds': 3... | {u'vqe_output': {u'fun_validated': -2.38661636... | False | 38.8053 | 1640 | 57.9185 | {u'fun_validated': -2.38661636391, u'nfev': 15... | |||
3accdce0b47ad82d | 0 | -0.561557 | -0.443228 | -0.561557 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 15 | 1 | {u'total_q_shots': 1640, u'total_q_seconds': 4... | {u'vqe_output': {u'fun_validated': -0.56155679... | False | 46.0758 | 1640 | 77.6178 | {u'fun_validated': -0.561556791508, u'nfev': 1... | |||||
7a3b8b947f7be3bf | 0 | -2.80778 | -2.79477 | -2.80778 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 300 | 1 | {u'total_q_shots': 1240, u'total_q_seconds': 3... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 33.3068 | 1240 | 78.8605 | {u'fun_validated': -2.80778395754, u'nfev': 30... | |||||
84e8a43e384b275b | 0 | -2.80778 | -2.7918 | -2.80778 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 400 | 1 | {u'total_q_shots': 1640, u'total_q_seconds': 4... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 43.3317 | 1640 | 61.6792 | {u'fun_validated': -2.80778395754, u'nfev': 40... | |||||
team-13 | my_grid_sampler | 50 | -1 | 80a6ae5dc4c96a97 | 0 | -2.80778 | -2.80777 | -2.80778 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 9 | 9 | {u'total_q_shots': 2000, u'total_q_seconds': 1... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 10.8516 | 2000 | 10.9098 | {u'fun_validated': -2.80778395754, u'nfev': 9,... | |
1 | -2.80778 | -2.80778 | -2.80778 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 9 | 9 | {u'total_q_shots': 2000, u'total_q_seconds': 1... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 13.1785 | 2000 | 13.242 | {u'fun_validated': -2.80778395754, u'nfev': 9,... | ||||||
2 | -2.80778 | -2.80777 | -2.80778 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 9 | 9 | {u'total_q_shots': 2000, u'total_q_seconds': 1... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 11.2444 | 2000 | 11.3005 | {u'fun_validated': -2.80778395754, u'nfev': 9,... | ||||||
3 | -2.80778 | -2.80778 | -2.80778 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 9 | 9 | {u'total_q_shots': 2000, u'total_q_seconds': 1... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 12.8025 | 2000 | 12.8539 | {u'fun_validated': -2.80778395754, u'nfev': 9,... | ||||||
4 | -2.80778 | -2.80778 | -2.80778 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 9 | 9 | {u'total_q_shots': 2000, u'total_q_seconds': 1... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 10.0433 | 2000 | 10.0984 | {u'fun_validated': -2.80778395754, u'nfev': 9,... | ||||||
5 | -2.80778 | -2.80776 | -2.80778 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 9 | 9 | {u'total_q_shots': 2000, u'total_q_seconds': 9... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 9.21545 | 2000 | 9.26805 | {u'fun_validated': -2.80778395754, u'nfev': 9,... | ||||||
6 | -2.80778 | -2.80777 | -2.80778 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 9 | 9 | {u'total_q_shots': 2000, u'total_q_seconds': 1... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 11.0507 | 2000 | 11.1043 | {u'fun_validated': -2.80778395754, u'nfev': 9,... | ||||||
7 | -2.80778 | -2.80778 | -2.80778 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 9 | 9 | {u'total_q_shots': 2000, u'total_q_seconds': 1... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 10.8229 | 2000 | 10.8743 | {u'fun_validated': -2.80778395754, u'nfev': 9,... | ||||||
8 | -2.80778 | -2.80773 | -2.80778 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 9 | 9 | {u'total_q_shots': 2000, u'total_q_seconds': 1... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 10.8434 | 2000 | 10.8991 | {u'fun_validated': -2.80778395754, u'nfev': 9,... | ||||||
9 | -2.80778 | -2.80778 | -2.80778 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 9 | 9 | {u'total_q_shots': 2000, u'total_q_seconds': 1... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 10.0792 | 2000 | 10.1351 | {u'fun_validated': -2.80778395754, u'nfev': 9,... | ||||||
10 | -2.80778 | -2.80777 | -2.80778 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 9 | 9 | {u'total_q_shots': 2000, u'total_q_seconds': 1... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 11.4874 | 2000 | 11.5408 | {u'fun_validated': -2.80778395754, u'nfev': 9,... | ||||||
9a184a90bb45cdfd | 0 | -2.80778 | -2.80756 | -2.80778 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 25 | 25 | {u'total_q_shots': 5200, u'total_q_seconds': 2... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 26.7785 | 5200 | 26.9063 | {u'fun_validated': -2.80778395754, u'nfev': 25... | |||||
100 | -1 | 55dbcff148a0acb7 | 0 | -2.80778 | -2.80777 | -2.80778 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 9 | 9 | {u'total_q_shots': 4000, u'total_q_seconds': 9... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 9.3016 | 4000 | 9.38857 | {u'fun_validated': -2.80778395754, u'nfev': 9,... | |||
a149b18c33b5fca3 | 0 | -2.80778 | -2.80778 | -2.80778 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 25 | 25 | {u'total_q_shots': 10400, u'total_q_seconds': ... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 25.8063 | 10400 | 26.0173 | {u'fun_validated': -2.80778395754, u'nfev': 25... | |||||
my_nelder_mead | 100 | 20 | 4556421aaf9faf07 | 0 | -0.996763 | -0.835669 | -0.898491 | {u'maxfev': 20} | def my_nelder_mead( func, x0, my_args=(), my_o... | 20 | 8 | {u'total_q_shots': 8000, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 21.6885 | 8000 | 21.8139 | {u'status': 1, u'success': False, u'final_simp... | ||
1 | -1.17927 | -0.987567 | -1.03888 | {u'maxfev': 20} | def my_nelder_mead( func, x0, my_args=(), my_o... | 20 | 9 | {u'total_q_shots': 8000, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 20.9041 | 8000 | 21.0318 | {u'status': 1, u'success': False, u'final_simp... | ||||||
2 | -1.0108 | -0.85176 | -0.842335 | {u'maxfev': 20} | def my_nelder_mead( func, x0, my_args=(), my_o... | 22 | 8 | {u'total_q_shots': 8800, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'success': Fal... | False | 24.7123 | 8800 | 24.8608 | {u'status': 1, u'success': False, u'final_simp... | ||||||
team-14 | my_minimizer | 100 | -1 | 570316f994d2da8d | 0 | 1.45153e-06 | -2.70565 | -2.75163 | {} | def my_minimizer( func, x0, my_args=(), my_opt... | 5 | -1 | {u'total_q_shots': 2000, u'total_q_seconds': 3... | {u'vqe_output': {u'fun': 1.45153325048e-06, u'... | False | 3.93702 | 2000 | 5.23373 | {u'fun': 1.45153325048e-06, u'nfev': 5, u'fun_... | |
1 | 1.6753e-06 | -2.43747 | -2.51297 | {} | def my_minimizer( func, x0, my_args=(), my_opt... | 5 | -1 | {u'total_q_shots': 2000, u'total_q_seconds': 5... | {u'vqe_output': {u'fun': 1.6753039269e-06, u'n... | False | 5.53016 | 2000 | 6.85521 | {u'fun': 1.6753039269e-06, u'nfev': 5, u'fun_v... | ||||||
2 | 1.37239e-06 | -2.69864 | -2.66739 | {} | def my_minimizer( func, x0, my_args=(), my_opt... | 5 | -1 | {u'total_q_shots': 2000, u'total_q_seconds': 3... | {u'vqe_output': {u'fun': 1.3723925889e-06, u'n... | False | 3.8056 | 2000 | 5.16632 | {u'fun': 1.3723925889e-06, u'nfev': 5, u'fun_v... |
df_metrics = get_metrics(df, delta=0.1, prob=0.5, which_fun_key='fun_exact', which_time_key='total_q_shots')
df_metrics
T_ave | T_err | num_repetitions | s | s_err | t_ave | t_err | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
platform | team | minimizer_method | sample_number | max_iterations | point | |||||||
8q-agave | team-07 | my_cobyla | 150 | 80 | 27bd6b1f8844e4a4 | 18800.000000 | 163.299316 | 3 | 1.000000 | 0.000000 | 18800.000000 | 163.299316 |
300 | 80 | 24e4f93212e61359 | 35600.000000 | 17811.980987 | 3 | 0.666667 | 0.272166 | 35600.000000 | 653.197265 | |||
team-10 | my_minimizer | 1 | 8 | 225212636444a960 | 64.000000 | 160.000000 | 3 | 0.333333 | 0.272166 | 32.000000 | 0.000000 | |
10 | 8 | a3842f89879d0961 | 640.000000 | 1600.000000 | 3 | 0.333333 | 0.272166 | 320.000000 | 0.000000 | |||
my_random_sampler | 100 | 10 | 251cad05f514492a | 8000.000000 | 20000.000000 | 3 | 0.333333 | 0.272166 | 4000.000000 | 0.000000 | ||
team-13 | my_grid_sampler | 50 | -1 | 6bc43f399ba11566 | 2000.000000 | 0.000000 | 1 | 1.000000 | 0.000000 | 2000.000000 | 0.000000 | |
b1c368d747a2b5fb | 5200.000000 | 0.000000 | 1 | 1.000000 | 0.000000 | 5200.000000 | 0.000000 | |||||
100 | -1 | 6f64ff6b83067e89 | 4000.000000 | 0.000000 | 1 | 1.000000 | 0.000000 | 4000.000000 | 0.000000 | |||
qvm | team-01 | my_cobyla | 50 | 80 | c6e2a72de42fba64 | 5733.333333 | 552.435684 | 3 | 1.000000 | 0.000000 | 5733.333333 | 552.435684 |
my_grid_sampler | 10 | -1 | 9dc38b214d7fbb38 | 4000.000000 | 0.000000 | 3 | 1.000000 | 0.000000 | 4000.000000 | 0.000000 | ||
team-03 | my_cobyla | 5 | 10 | aa64cff4b1d94d38 | 400.000000 | 1000.000000 | 3 | 0.333333 | 0.272166 | 200.000000 | 0.000000 | |
10 | 10 | 4ffebda70760cefc | 400.000000 | 200.000000 | 3 | 0.666667 | 0.272166 | 400.000000 | 0.000000 | |||
50 | 30 | 3c1a71fb284ef7a3 | 4400.000000 | 222.222222 | 9 | 1.000000 | 0.000000 | 4400.000000 | 222.222222 | |||
100 | 80 | bb013002e7edb3a3 | 10266.666667 | 891.108340 | 3 | 1.000000 | 0.000000 | 10266.666667 | 891.108340 | |||
my_minimizer | 5 | 10 | 2b74910afe36e6b5 | 786.666667 | 1969.587285 | 3 | 0.333333 | 0.272166 | 393.333333 | 53.610391 | ||
9f9bf704d869c823 | 1840.000000 | 4652.722023 | 3 | 0.333333 | 0.272166 | 920.000000 | 349.221356 | |||||
10 | 30 | 314e14a49a71437e | 1653.333333 | 912.010396 | 3 | 0.666667 | 0.272166 | 1653.333333 | 385.207977 | |||
team-06 | my_cobyla | 75 | 80 | 393eeec44fb694bc | 5900.000000 | 81.649658 | 3 | 1.000000 | 0.000000 | 5900.000000 | 81.649658 | |
100 | 80 | 7229f2ac1a4f408f | 7866.666667 | 203.670031 | 6 | 1.000000 | 0.000000 | 7866.666667 | 203.670031 | |||
team-07 | my_cobyla | 150 | 80 | 114a1dbc72e659cb | 12600.000000 | 1232.882801 | 3 | 1.000000 | 0.000000 | 12600.000000 | 1232.882801 | |
my_nelder_mead | 150 | 80 | 9629cdd4afb92cba | 95600.000000 | 239022.314161 | 3 | 0.333333 | 0.272166 | 47800.000000 | 1632.993162 | ||
team-08 | my_nelder_mead | 100 | 80 | ec34936968a6ded1 | 60800.000000 | 152022.454482 | 3 | 0.333333 | 0.272166 | 30400.000000 | 1306.394529 | |
team-09 | my_cobyla | 10 | 80 | a95e224f9d46c9bc | 2080.000000 | 5203.827651 | 3 | 0.333333 | 0.272166 | 1040.000000 | 99.777530 | |
16 | 80 | ea30144a65b56ed1 | 1685.333333 | 845.542210 | 3 | 0.666667 | 0.272166 | 1685.333333 | 69.674375 | |||
18 | 80 | 629ceefcd1714629 | 1584.000000 | 179.599555 | 3 | 1.000000 | 0.000000 | 1584.000000 | 179.599555 | |||
20 | 80 | d44f28297a82c9e2 | 2026.666667 | 1023.342381 | 3 | 0.666667 | 0.272166 | 2026.666667 | 142.776697 | |||
40 | 80 | ab01a50c47a6ac7e | 3626.666667 | 428.883132 | 3 | 1.000000 | 0.000000 | 3626.666667 | 428.883132 | |||
100 | 80 | 8549d1ba56d120a4 | 10266.666667 | 5151.770641 | 3 | 0.666667 | 0.272166 | 10266.666667 | 435.464843 | |||
140 | 80 | 46f4687bd0fc29c9 | 10640.000000 | 263.986532 | 3 | 1.000000 | 0.000000 | 10640.000000 | 263.986532 | |||
200 | 80 | 045e5aad57c12b50 | 18133.333333 | 1427.766969 | 3 | 1.000000 | 0.000000 | 18133.333333 | 1427.766969 | |||
team-10 | my_cobyla | 100 | 80 | 78fc1d13c58f82a3 | 9466.666667 | 1072.207829 | 3 | 1.000000 | 0.000000 | 9466.666667 | 1072.207829 | |
team-11 | my_cobyla | 100 | 80 | 35ba631d4fd7a6fd | 8266.666667 | 288.032920 | 3 | 1.000000 | 0.000000 | 8266.666667 | 288.032920 | |
team-12 | my_minimizer | 1 | 1 | 1f72b8e0f40fdb44 | 284.000000 | 0.000000 | 1 | 1.000000 | 0.000000 | 284.000000 | 0.000000 | |
5 | 1 | 2d241af9ac061a2b | 820.000000 | 0.000000 | 1 | 1.000000 | 0.000000 | 820.000000 | 0.000000 | |||
10 | 1 | 7a3b8b947f7be3bf | 1240.000000 | 0.000000 | 1 | 1.000000 | 0.000000 | 1240.000000 | 0.000000 | |||
84e8a43e384b275b | 1640.000000 | 0.000000 | 1 | 1.000000 | 0.000000 | 1640.000000 | 0.000000 | |||||
team-13 | my_grid_sampler | 50 | -1 | 80a6ae5dc4c96a97 | 2000.000000 | 0.000000 | 11 | 1.000000 | 0.000000 | 2000.000000 | 0.000000 | |
9a184a90bb45cdfd | 5200.000000 | 0.000000 | 1 | 1.000000 | 0.000000 | 5200.000000 | 0.000000 | |||||
100 | -1 | 55dbcff148a0acb7 | 4000.000000 | 0.000000 | 1 | 1.000000 | 0.000000 | 4000.000000 | 0.000000 | |||
a149b18c33b5fca3 | 10400.000000 | 0.000000 | 1 | 1.000000 | 0.000000 | 10400.000000 | 0.000000 |
Somewhat unexpectedly, the winner obtained the exact answer (!) with sample_number=1
(!!) on real hardware (!!!). Please see below an explanation why this was the case.
idxmin1 = df_metrics['T_ave'].idxmin()
idxmin1
(u'8q-agave', u'team-10', u'my_minimizer', 1, 8, '225212636444a960')
df_metrics.loc[[idxmin1]]
T_ave | T_err | num_repetitions | s | s_err | t_ave | t_err | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
platform | team | minimizer_method | sample_number | max_iterations | point | |||||||
8q-agave | team-10 | my_minimizer | 1 | 8 | 225212636444a960 | 64.0 | 160.0 | 3 | 0.333333 | 0.272166 | 32.0 | 0.0 |
df.loc[idxmin1]
fun | fun_exact | fun_validated | minimizer_options | minimizer_src | nfev | nit | report | run | success | total_q_seconds | total_q_shots | total_seconds | vqe_output | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
repetition_id | ||||||||||||||
0 | -2.80778 | -2.74963 | -2.80778 | {u'maxfev': 8} | def my_minimizer( func, x0, my_args=(), my_opt... | 8 | 8 | {u'total_q_shots': 32, u'total_q_seconds': 69.... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 69.9116 | 32 | 100.557 | {u'fun_validated': -2.80778395754, u'nfev': 8,... |
1 | -3.33564 | -2.57288 | -1.40389 | {u'maxfev': 8} | def my_minimizer( func, x0, my_args=(), my_opt... | 8 | 8 | {u'total_q_shots': 32, u'total_q_seconds': 60.... | {u'vqe_output': {u'fun_validated': -1.40389197... | False | 60.8164 | 32 | 60.8258 | {u'fun_validated': -1.40389197877, u'nfev': 8,... |
2 | -2.80778 | -2.6881 | -2.80778 | {u'maxfev': 8} | def my_minimizer( func, x0, my_args=(), my_opt... | 8 | 8 | {u'total_q_shots': 32, u'total_q_seconds': 46.... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 46.7979 | 32 | 46.8075 | {u'fun_validated': -2.80778395754, u'nfev': 8,... |
# Platform, Team, minimizer Function, number of Samples, number of function eValuations, Experiment, Repetition
(p,t,f,s,v,e) = idxmin1
# Plot the winner.
plot(df.loc[[(p,t,f,s,v,e,k) for k in range(len(df.loc[idxmin1]))]],
xmax=7, xstep=1, ymin=-3.00, ymax=0.00+0.01, legend_loc='upper center')
# Exclude the winner.
df_metrics = df_metrics.drop(idxmin1)
The Hamiltonian of Helium in the STO-3G basis is given by:
\begin{equation} H = -1.6678202144537553 + 0.7019459893849936 \cdot Z_0 + 0.7019459893849936 \cdot Z_1 + 0.263928235683768058 \cdot Z_0 \cdot Z_1 \end{equation}Since the Hamiltonian consists of a sum of commuting operators ($Z_0$, $Z_1$, $Z_0\cdot Z1$), there exists a simultaneous eigenbasis (including the ground state) on which the value of each of the operators is $+1$ or $-1$.
When sample_number=1
, measuring an operator once in any state also results in $+1$ or $-1$. This implies that one get "lucky" with any mininizer method even on noisy hardware. (Indeed, we have been able to reproduce this result with my_nelder_mead
.)
For more complex molecules, the Hamiltonian is unlikely to consist of a sum of commuting operators, hence picking up a good optimizer and its parameters will be crucial for success.
The runner-up also used sample_number=1
but only a single run (hence, it's a "conditional" runner-up, as we can determine the error only from multiple runs).
idxmin2 = df_metrics['T_ave'].idxmin()
idxmin2
(u'qvm', u'team-12', u'my_minimizer', 1, 1, '1f72b8e0f40fdb44')
df_metrics.loc[[idxmin2]]
T_ave | T_err | num_repetitions | s | s_err | t_ave | t_err | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
platform | team | minimizer_method | sample_number | max_iterations | point | |||||||
qvm | team-12 | my_minimizer | 1 | 1 | 1f72b8e0f40fdb44 | 284.0 | 0.0 | 1 | 1.0 | 0.0 | 284.0 | 0.0 |
df.loc[idxmin2]
fun | fun_exact | fun_validated | minimizer_options | minimizer_src | nfev | nit | report | run | success | total_q_seconds | total_q_shots | total_seconds | vqe_output | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
repetition_id | ||||||||||||||
0 | -2.80778 | -2.71276 | -2.80778 | {u'maxfev': 1} | def my_minimizer( func, x0, my_args=(), my_opt... | 70 | 1 | {u'total_q_shots': 284, u'total_q_seconds': 74... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 74.4332 | 284 | 116.381 | {u'fun_validated': -2.80778395754, u'nfev': 70... |
# Platform, Team, minimizer Function, number of Samples, number of function eValuations, Experiment, Repetition
(p,t,f,s,v,e) = idxmin2
# Plot the winner.
plot(df.loc[[(p,t,f,s,v,e,k) for k in range(len(df.loc[idxmin2]))]],
xmax=7, xstep=1, ymin=-3.00, ymax=0.00+0.01, legend_loc='upper center')
# Print the minimizer source.
print(df.loc[(p,t,f,s,v,e,0)]['minimizer_src'])
def my_minimizer( func, x0, my_args=(), my_options=None ): "Your own attempt at writing a stochastic minimizer" global invocations print("x0 is", x0) print("func is", inspect.getsource(func)) para_func = mapf(func) invocations = 0 def wp(*test): global invocations args = test[0][0] print("invoking with", args) ret = func(args, my_args) print("evaluated as", ret) invocations += SAMPLE_SZ return ret myProblem = GPyOpt.methods.BayesianOptimization(wp, domain=bounds, acquisition_type = 'MPI', initial_design_numdata = 30, Initial_design_type="latin", evaluator_type = 'local_penalization', num_cores=1, batch_size=1) myProblem.run_optimization(40) myProblem.plot_acquisition() current_func_value = func(myProblem.x_opt, my_args) minimizer_output = { 'fun' : current_func_value, 'nfev' : invocations, 'nit' : 1, 'x' : myProblem.x_opt } return minimizer_output
# Exclude the conditional runner-up.
df_metrics = df_metrics.drop(idxmin2)
platform_qvm = 'qvm'
df_metrics_qvm = df_metrics.loc[platform_qvm]
idxmin_qvm = df_metrics_qvm['T_ave'].idxmin()
idxmin_qvm
(u'team-03', u'my_cobyla', 5, 10, 'aa64cff4b1d94d38')
df_metrics.loc[platform_qvm].loc[[idxmin_qvm]]
T_ave | T_err | num_repetitions | s | s_err | t_ave | t_err | |||||
---|---|---|---|---|---|---|---|---|---|---|---|
team | minimizer_method | sample_number | max_iterations | point | |||||||
team-03 | my_cobyla | 5 | 10 | aa64cff4b1d94d38 | 400.0 | 1000.0 | 3 | 0.333333 | 0.272166 | 200.0 | 0.0 |
df.loc[platform_qvm].loc[idxmin_qvm]
fun | fun_exact | fun_validated | minimizer_options | minimizer_src | nfev | nit | report | run | success | total_q_seconds | total_q_shots | total_seconds | vqe_output | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
repetition_id | ||||||||||||||
0 | -2.80778 | -2.57372 | -2.80778 | {u'maxiter': 10} | def my_cobyla( func, x0, my_args=(), my_option... | 10 | -1 | {u'total_q_shots': 200, u'total_q_seconds': 8.... | {u'vqe_output': {u'status': 2, u'maxcv': 0.0, ... | False | 8.9148 | 200 | 8.96425 | {u'status': 2, u'maxcv': 0.0, u'success': Fals... |
1 | -2.80778 | -2.57372 | -2.52701 | {u'maxiter': 10} | def my_cobyla( func, x0, my_args=(), my_option... | 10 | -1 | {u'total_q_shots': 200, u'total_q_seconds': 8.... | {u'vqe_output': {u'status': 2, u'maxcv': 0.0, ... | False | 8.49102 | 200 | 8.55271 | {u'status': 2, u'maxcv': 0.0, u'success': Fals... |
2 | -2.80778 | -2.80174 | -2.80778 | {u'maxiter': 10} | def my_cobyla( func, x0, my_args=(), my_option... | 10 | -1 | {u'total_q_shots': 200, u'total_q_seconds': 8.... | {u'vqe_output': {u'status': 2, u'maxcv': 0.0, ... | False | 8.07 | 200 | 8.10312 | {u'status': 2, u'maxcv': 0.0, u'success': Fals... |
# Platform, Team, minimizer Function, number of Samples, number of function eValuations, Experiment, Repetition
p = platform_qvm
(t,f,s,v,e) = idxmin_qvm
# Plot the runner up.
plot(df.loc[[(p,t,f,s,v,e,k) for k in range(len(df.loc[(p,t,f,s,v,e)]))]],
xmax=9, xstep=1, ymin=-3.00, ymax=0.00+0.01, legend_loc='upper right')
# Print the minimizer source.
print(df.loc[(p,t,f,s,v,e,0)]['minimizer_src'])
def my_cobyla( func, x0, my_args=(), my_options=None ): # another non-stochastic minimizer for comparison "COBYLA optimizer from SciPy library" return minimize( func, x0, method='COBYLA', options=my_options, args=my_args )
# Exclude the QVM runner-up.
df_metrics = df_metrics.drop((p,t,f,s,v,e))
platform_qpu = '8q-agave'
df_metrics_qpu = df_metrics.loc[platform_qpu]
idxmin_qpu = df_metrics_qpu['T_ave'].idxmin()
idxmin_qpu
(u'team-10', u'my_minimizer', 10, 8, 'a3842f89879d0961')
df_metrics_qpu.loc[[idxmin_qpu]]
T_ave | T_err | num_repetitions | s | s_err | t_ave | t_err | |||||
---|---|---|---|---|---|---|---|---|---|---|---|
team | minimizer_method | sample_number | max_iterations | point | |||||||
team-10 | my_minimizer | 10 | 8 | a3842f89879d0961 | 640.0 | 1600.0 | 3 | 0.333333 | 0.272166 | 320.0 | 0.0 |
df.loc[platform_qpu].loc[idxmin_qpu]
fun | fun_exact | fun_validated | minimizer_options | minimizer_src | nfev | nit | report | run | success | total_q_seconds | total_q_shots | total_seconds | vqe_output | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
repetition_id | ||||||||||||||
0 | -3.01893 | -2.43453 | -2.51016 | {u'maxfev': 8} | def my_minimizer( func, x0, my_args=(), my_opt... | 8 | 8 | {u'total_q_shots': 320, u'total_q_seconds': 49... | {u'vqe_output': {u'fun_validated': -2.51015540... | False | 49.4448 | 320 | 49.4587 | {u'fun_validated': -2.51015540172, u'nfev': 8,... |
1 | -2.22938 | -2.71485 | -2.36977 | {u'maxfev': 8} | def my_minimizer( func, x0, my_args=(), my_opt... | 8 | 8 | {u'total_q_shots': 320, u'total_q_seconds': 10... | {u'vqe_output': {u'fun_validated': -2.36976620... | False | 109.043 | 320 | 109.057 | {u'fun_validated': -2.36976620384, u'nfev': 8,... |
2 | -2.51016 | -2.49231 | -1.79024 | {u'maxfev': 8} | def my_minimizer( func, x0, my_args=(), my_opt... | 8 | 8 | {u'total_q_shots': 320, u'total_q_seconds': 51... | {u'vqe_output': {u'fun_validated': -1.79024166... | False | 51.8971 | 320 | 82.2221 | {u'fun_validated': -1.7902416688, u'nfev': 8, ... |
# Platform, Team, Experiment, minimizer Function, number of Samples, number of function eValuations, Repetition
p = platform_qpu
(t,f,s,v,e) = idxmin_qpu
# Plot the QPU runner-up.
plot(df.loc[[(p,t,f,s,v,e,k) for k in range(len(df.loc[(p,t,f,s,v,e)]))]],
xmax=7, xstep=1, ymin=-3.00, ymax=-0.00+0.01, legend_loc='upper right')
# Print the minimizer source.
print(df.loc[(p,t,f,s,v,e,0)]['minimizer_src'])
def my_minimizer( func, x0, my_args=(), my_options=None ): "Your own attempt at writing a stochastic minimizer" my_options = my_options or {} points = my_options.get('maxfev', 30) # by default perform 30 function evaluations num_parameters = len(x0) # get number of parameters needed for objective function flist = [0.] xlist = [] t = 0.001 for i in range(points): x = [random.random() for i in range(num_parameters)] # initialise parameters randomly from uniform distribution fnew = func(x, my_args) flist.append(fnew) xlist.append(x) # SA Algorithm d = flist[i+1] - flist[i] psa = np.exp(-d/t) if d < 0.0: flist[i] = np.copy(flist[i+1]) else: if psa>np.random.random(): flist[i] = np.copy(flist[i+1]) else: x = [random.random() for i in range(num_parameters)] t *= 0.9 min_idx = np.argmin(flist) best_x = xlist[min_idx] fmin = flist[min_idx] # current_func_value = func(x0, my_args) # minimizer_output = { 'fun' : current_func_value, 'nfev' : 1, 'nit' : 1, 'x' : x0 } minimizer_output = { 'fun' : fmin, 'nfev' : points, 'nit' : points, 'x' : best_x } return minimizer_output
# Exclude the QPU runner-up.
df_metrics = df_metrics.drop((p,t,f,s,v,e))
df_metrics_prob100 = get_metrics(df, delta=0.1, prob=0.999, which_fun_key='fun_exact', which_time_key='total_q_shots')
df_metrics_prob100 = df_metrics_prob100[(df_metrics_prob100['s']==1) & (df_metrics_prob100['num_repetitions']>1)]
idxmin_prob100 = df_metrics_prob100['T_ave'].idxmin()
idxmin_prob100
(u'qvm', u'team-09', u'my_cobyla', 18, 80, '629ceefcd1714629')
df_metrics_prob100.loc[[idxmin_prob100]]
T_ave | T_err | num_repetitions | s | s_err | t_ave | t_err | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
platform | team | minimizer_method | sample_number | max_iterations | point | |||||||
qvm | team-09 | my_cobyla | 18 | 80 | 629ceefcd1714629 | 1584.0 | 179.599555 | 3 | 1.0 | 0.0 | 1584.0 | 179.599555 |
df.loc[idxmin_prob100]
fun | fun_exact | fun_validated | minimizer_options | minimizer_src | nfev | nit | report | run | success | total_q_seconds | total_q_shots | total_seconds | vqe_output | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
repetition_id | ||||||||||||||
0 | -2.80778 | -2.71476 | -2.80778 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 28 | -1 | {u'total_q_shots': 2016, u'total_q_seconds': 2... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 27.1409 | 2016 | 27.2655 | {u'status': 1, u'maxcv': 0.0, u'success': True... |
1 | -2.80778 | -2.79107 | -2.80778 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 20 | -1 | {u'total_q_shots': 1440, u'total_q_seconds': 1... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 19.1101 | 1440 | 19.1999 | {u'status': 1, u'maxcv': 0.0, u'success': True... |
2 | -2.80778 | -2.79924 | -2.80778 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 18 | -1 | {u'total_q_shots': 1296, u'total_q_seconds': 1... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 17.3081 | 1296 | 17.3906 | {u'status': 1, u'maxcv': 0.0, u'success': True... |
# Platform, Team, Experiment, minimizer Function, number of Samples, number of function eValuations, Repetition
(p,t,f,s,v,e) = idxmin_prob100
# Plot.
plot(df.loc[[(p,t,f,s,v,e,k) for k in range(len(df.loc[(p,t,f,s,v,e)]))]],
xmax=30, xstep=1, ymin=-3.00, ymax=-0.00+0.01, legend_loc='upper right')
idxmax_prob100 = df_metrics_prob100['T_ave'].idxmax()
idxmax_prob100
(u'8q-agave', u'team-07', u'my_cobyla', 150, 80, '27bd6b1f8844e4a4')
df_metrics_prob100.loc[[idxmax_prob100]]
T_ave | T_err | num_repetitions | s | s_err | t_ave | t_err | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
platform | team | minimizer_method | sample_number | max_iterations | point | |||||||
8q-agave | team-07 | my_cobyla | 150 | 80 | 27bd6b1f8844e4a4 | 18800.0 | 163.299316 | 3 | 1.0 | 0.0 | 18800.0 | 163.299316 |
df.loc[idxmax_prob100]
fun | fun_exact | fun_validated | minimizer_options | minimizer_src | nfev | nit | report | run | success | total_q_seconds | total_q_shots | total_seconds | vqe_output | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
repetition_id | ||||||||||||||
0 | -2.19651 | -2.80078 | -2.20924 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 32 | -1 | {u'total_q_shots': 19200, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 253.491 | 19200 | 253.973 | {u'status': 1, u'maxcv': 0.0, u'success': True... |
1 | -2.20819 | -2.79951 | -2.27849 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 31 | -1 | {u'total_q_shots': 18600, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 236.087 | 18600 | 327.784 | {u'status': 1, u'maxcv': 0.0, u'success': True... |
2 | -2.0234 | -2.80312 | -2.23514 | {u'maxiter': 80} | def my_cobyla( func, x0, my_args=(), my_option... | 31 | -1 | {u'total_q_shots': 18600, u'total_q_seconds': ... | {u'vqe_output': {u'status': 1, u'maxcv': 0.0, ... | True | 220.162 | 18600 | 220.468 | {u'status': 1, u'maxcv': 0.0, u'success': True... |
# Platform, Team, Experiment, minimizer Function, number of Samples, number of function eValuations, Repetition
(p,t,f,s,v,e) = idxmax_prob100
# Plot.
plot(df.loc[[(p,t,f,s,v,e,k) for k in range(len(df.loc[(p,t,f,s,v,e)]))]],
xmax=31, xstep=1, ymin=-3.00, ymax=-0.00+0.01, legend_loc='upper right')
# The ratio of the worst of the best with 100% convergence.
df_metrics_prob100.loc[idxmax_prob100]['T_ave'] / df_metrics_prob100.loc[idxmin_prob100]['T_ave']
11.868686868686869
# Exclude the best entry with 100% convergence.
df_metrics_prob100 = df_metrics_prob100.drop(idxmin_prob100)
df_metrics_delta0 = get_metrics(df, delta=0.01, prob=0.999, which_fun_key='fun_exact', which_time_key='total_q_shots')
df_metrics_delta0 = df_metrics_delta0[(df_metrics_delta0['num_repetitions']>1)]
idxmin_delta0 = df_metrics_delta0['T_ave'].idxmin()
idxmin_delta0
(u'qvm', u'team-13', u'my_grid_sampler', 50, -1, '80a6ae5dc4c96a97')
# Also, the runner-up entry with 100% convergence!
idxmin_prob100 = df_metrics_prob100['T_ave'].idxmin()
idxmin_prob100
(u'qvm', u'team-13', u'my_grid_sampler', 50, -1, '80a6ae5dc4c96a97')
df_metrics_delta0.loc[[idxmin_delta0]]
T_ave | T_err | num_repetitions | s | s_err | t_ave | t_err | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
platform | team | minimizer_method | sample_number | max_iterations | point | |||||||
qvm | team-13 | my_grid_sampler | 50 | -1 | 80a6ae5dc4c96a97 | 2000.0 | 0.0 | 11 | 1.0 | 0.0 | 2000.0 | 0.0 |
df.loc[idxmin_delta0]
fun | fun_exact | fun_validated | minimizer_options | minimizer_src | nfev | nit | report | run | success | total_q_seconds | total_q_shots | total_seconds | vqe_output | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
repetition_id | ||||||||||||||
0 | -2.80778 | -2.80777 | -2.80778 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 9 | 9 | {u'total_q_shots': 2000, u'total_q_seconds': 1... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 10.8516 | 2000 | 10.9098 | {u'fun_validated': -2.80778395754, u'nfev': 9,... |
1 | -2.80778 | -2.80778 | -2.80778 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 9 | 9 | {u'total_q_shots': 2000, u'total_q_seconds': 1... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 13.1785 | 2000 | 13.242 | {u'fun_validated': -2.80778395754, u'nfev': 9,... |
2 | -2.80778 | -2.80777 | -2.80778 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 9 | 9 | {u'total_q_shots': 2000, u'total_q_seconds': 1... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 11.2444 | 2000 | 11.3005 | {u'fun_validated': -2.80778395754, u'nfev': 9,... |
3 | -2.80778 | -2.80778 | -2.80778 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 9 | 9 | {u'total_q_shots': 2000, u'total_q_seconds': 1... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 12.8025 | 2000 | 12.8539 | {u'fun_validated': -2.80778395754, u'nfev': 9,... |
4 | -2.80778 | -2.80778 | -2.80778 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 9 | 9 | {u'total_q_shots': 2000, u'total_q_seconds': 1... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 10.0433 | 2000 | 10.0984 | {u'fun_validated': -2.80778395754, u'nfev': 9,... |
5 | -2.80778 | -2.80776 | -2.80778 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 9 | 9 | {u'total_q_shots': 2000, u'total_q_seconds': 9... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 9.21545 | 2000 | 9.26805 | {u'fun_validated': -2.80778395754, u'nfev': 9,... |
6 | -2.80778 | -2.80777 | -2.80778 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 9 | 9 | {u'total_q_shots': 2000, u'total_q_seconds': 1... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 11.0507 | 2000 | 11.1043 | {u'fun_validated': -2.80778395754, u'nfev': 9,... |
7 | -2.80778 | -2.80778 | -2.80778 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 9 | 9 | {u'total_q_shots': 2000, u'total_q_seconds': 1... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 10.8229 | 2000 | 10.8743 | {u'fun_validated': -2.80778395754, u'nfev': 9,... |
8 | -2.80778 | -2.80773 | -2.80778 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 9 | 9 | {u'total_q_shots': 2000, u'total_q_seconds': 1... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 10.8434 | 2000 | 10.8991 | {u'fun_validated': -2.80778395754, u'nfev': 9,... |
9 | -2.80778 | -2.80778 | -2.80778 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 9 | 9 | {u'total_q_shots': 2000, u'total_q_seconds': 1... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 10.0792 | 2000 | 10.1351 | {u'fun_validated': -2.80778395754, u'nfev': 9,... |
10 | -2.80778 | -2.80777 | -2.80778 | {} | def my_grid_sampler( func, x0, my_args=(), my_... | 9 | 9 | {u'total_q_shots': 2000, u'total_q_seconds': 1... | {u'vqe_output': {u'fun_validated': -2.80778395... | False | 11.4874 | 2000 | 11.5408 | {u'fun_validated': -2.80778395754, u'nfev': 9,... |
# Platform, Team, Experiment, minimizer Function, number of Samples, number of function eValuations, Repetition
(p,t,f,s,v,e) = idxmin_delta0
# Plot.
plot(df.loc[[(p,t,f,s,v,e,k) for k in range(len(df.loc[(p,t,f,s,v,e)]))]],
xmax=9, xstep=1, ymin=-3.00, ymax=-0.00+0.01, legend_loc='upper right')
# Print the minimizer source.
print(df.loc[(p,t,f,s,v,e,0)]['minimizer_src'])
def my_grid_sampler( func, x0, my_args=(), my_options=None ): "Simple optimiser: samples on a grid and returns the minimum - used here as an example" my_options = my_options or {} points = 3 # Number of points in each dimension num_parameters = 2 #len(x0) # get number of parameters needed for objective function flist = [] xlist = [] xgrid = [] fgrid = [] for i in range(points): xrow = [] frow = [] for j in range(points): x = [2*np.pi*i/points, 2*np.pi*j/points] # Sample a point from a uniform grid fnew = func(x, my_args) flist.append(fnew) xlist.append(x) xrow.append(x) frow.append(fnew) xgrid.append(xrow) fgrid.append(frow) min_idx = np.argmin(flist) best_x = xlist[min_idx] fmin = flist[min_idx] print(fgrid) FTgrid = np.fft.fft2(fgrid) print(FTgrid) def wrp(x): return fn(FTgrid.shape[0], FTgrid, x[0], x[1]) res = minimize(wrp, best_x, method='nelder-mead', options={'xtol':1e-8, 'disp':False}) minf = func(res.x, my_args) minimizer_output = { 'fun' : minf, 'nfev' : points*points, 'nit' : points*points, 'x' : res.x } return minimizer_output
# # Plot all.
# plot(df, legend_loc='center')
# Plot QPU only.
plot(df, platform_set=[platform_qpu], markersize_divisor=10,
xmin=0, xmax=34+0.01, xstep=1, ymin=-2.80, ymax=-1.80-0.01, ystep=0.05, legend_loc='lower left')
# Plot COBYLA only.
plot(df, minimizer_method_set=['my_cobyla'], sample_number_set=[50], markersize_divisor=5,
xmin=5, xmax=32+0.01, xstep=1, ymin=-2.89, ymax=-1.79+0.01, ystep=0.05, legend_loc='upper right')
# plot_metric(df)
# plot_metric(df, metric='total_q_shots')