# Accelerated Random Benchmarking¶

## Christopher Granade Institute for Quantum Computing¶

Joint Work with Christopher Ferrie and D. G. Cory

### Abstract¶

Producing useful quantum information devices requires efficiently assessing control of quantum systems, so that we can determine whether we have implemented a desired gate, and refine accordingly. Randomized benchmarking uses symmetry to reduce the difficulty of this task.

We bound the resources required for benchmarking and show that with prior information, orders of magnitude in accuracy can be obtained. We reach these accuracies with near-optimal resources, improving dramatically on curve fitting. Finally, we show that our approach is useful for physical devices by comparing to simulations.


## Compling and Hosting¶

To compile these slides, we use nbconvert.

In [3]:
!ipython nbconvert --to slides --template slides.tpl slides.ipynb
!mv slides.slides.html slides.html

[NbConvertApp] Using existing profile dir: u'/home/cgranade/.ipython/profile_default'
[NbConvertApp] Converting notebook slides.ipynb to slides
[NbConvertApp] Support files will be in slides_files/
[NbConvertApp] Writing 225569 bytes to slides.slides.html


If you want to view them in your browser complete with speaker notes, remote control support, etc., then you need to host the slides. The instructions for Reveal.js include directions for hosting via a library called Grunt. Unfortunately, this doesn't work well with remot.io, as that tool requires that you serve from port 80.

## Configuration¶

Since we're going to display some <iframe>s in this talk, we'll need to import the display functionality from IPython and write a small function. These have no part in the talk itself, so we mark these cells as Skip in the Cell Toolbar.

In [2]:
from IPython.display import HTML

In [3]:
def iframe(src):
return HTML('<iframe src="{}" width=1000 height=400></iframe>'.format(src))


# Introduction¶

Fully characterizing large quantum systems is very difficult.

• Exponentially many parameters.
• Classical simulation of large quantum systems is intractable.
• State preparation and measurement (SPAM) errors.

For some applications, fidelity alone can be useful. Ex:

• Feedback for refining control.

Fidelity isn't the full story, though (Puzzuoli et al, PRA 89 022306), so some care is needed.

# Twirling¶

• The Clifford group is a unitary 2-design, so conjugating a map $\Lambda$ by random Cliffords samples from the Haar averaged channel $$W[\Lambda](\rho) = \int U \Lambda(U^\dagger \rho U) U^\dagger\ \dd U.$$
• $W$ is a superchannel that maps quantum channels to depolarizing channels that preserve their inputs with probability $$p = \frac{d F - 1} {d - 1},$$ where $F$ is the fidelity of $\Lambda$.

Knill et al, PRA 77 012307 (2008). Magesan, Gambetta and Emerson, PRA 85 042311 (2012). Wood, in preparation.

# Randomized Benchmarking¶

• Can't tell with one instance how much of the error is due to SPAM, how much is due to imperfect twirling.
• The decay of fidelity with sequence length gives info, even w/ SPAM, imperfect gates.
• Choose each sequence such that ideal action is identity.

• Used in a wide range of experiments to characterize gatesets.

# Zeroth-Order Model¶

$$F_g(m) = A p^m + B.$$

• $A$, $B$ are constants describing SPAM errors. $F_g(m)$ is the average fidelity of a sequence of length $m$.
• $1 - p$ is then the depolarizing strength of $$W[\Lambda] = W\left[\expect_{C \sim \mathcal{C}} [\Lambda_C] \right],$$ taken over the implementation of each Clifford gate $C$.

Knill et al, PRA 77 012307 (2008). Magesan, Gambetta and Emerson, PRA 85 042311 (2012).

# Interleaved Protocol¶

• Can probe performance of a desired gate by interleaving it into the twirling sequences.
• Effective depolarizing parameter $p_{\bar{\mathcal{C}}} := \tilde{p} p_{\text{ref}}$.
• $\tilde{p}$: depolarizing parameter for the gate of interest.
• $p_{\text{ref}}$: expected depolarizing parameter, taken over gateset.

For example, to measure the fidelity of $S_C$:

Magesan et al, PRL 109 080505 (2012).

# Example of Interleaved Model¶

$\tilde{p} = 0.99994$, $p_{\text{ref}} = 0.99999$

# Finite Sampling¶

• Randomized benchmarking data is 0/1 data: was the initial state preserved by a random sequence of length $m$ or not?
• Conventional approach: estimate $\hat{F}_g(m)$ using $K$ sequences for each of many $m$.
• Epstein et al (1308.2928) investigate error bounds due to finite sampling.
• Known from frequency estimation examples that 1 bit/experiment limit can be useful.

# Cramer-Rao Bound¶

• We extend by using Fisher information to investigate limit as $K \to 1$.
• Mean (taken over data) error matrix $\expect_D[(\hat{\vec{x}} - \vec{x}) (\hat{\vec{x}} - \vec{x})^\T]$ for unbiased estimators bounded by Cramer-Rao Bound: $$\matr{E}(\vec{x}) \ge \matr{I}^{-1}(\vec{x}),$$ where $$\matr{I}(\vec{x}) := \expect_{D | \vec{x}} [\nabla_{\vec{x}} \log\Pr(D | \vec{x}) \cdot \nabla_{\vec{x}}^\T \log\Pr(D | \vec{x}) ]$$ is the Fisher information at $\vec{x}$.

• Here, $\vec{x} = (p, A, B)$ or $(\tilde{p}, p_{\text{ref}}, A, B)$ are the unknown parameters being estimated.

Ferrie and Granade, QIP 12 611 (2012).

# Experiment Design¶

• Using Fisher information, we can choose most informative $m$ for hypotheses about $A$ and $B$.

# Bayesian Approach¶

In practice, we often have prior information. Demanding unbiased estimators is too strong.

Let's take a Bayesian approach instead. After observing a datum $d$ taken from a sequence of length $m$: $$\Pr(\vec{x} | d; m) = \frac{\Pr(d | \vec{x}; m)}{\Pr(d | m)} \Pr(\vec{x}).$$

We can implement this on a computer using sequential Monte Carlo (SMC). For example, to incorporate a uniform prior:

In [9]:
from qinfer.smc import SMCUpdater
from qinfer.rb import RandomizedBenchmarkingModel
from qinfer.distributions import UniformDistribution

prior = UniformDistribution([[0.9, 1], [0.4, 0.5], [0.5, 0.6]])
updater = SMCUpdater(RandomizedBenchmarkingModel(), 10000, prior)

# As data arrives:
# updater.update(datum, experiment)


Granade et al, NJP 14 103013 (2012).

# Bayesian Cramer-Rao Bound¶

With prior information, we need the Bayesian Cramer-Rao Bound, $$\expect_{\vec{x}} [\matr{E}(\vec{x})] \ge \matr{J}^{-1},$$ where $$\matr{J} := \expect_{\vec{x}} [\matr{I}(\vec{x})]$$ is the Bayesian information matrix.

This again can also be computed by using SMC.

In [5]:
from qinfer.smc import SMCUpdaterBCRB
updater = SMCUpdaterBCRB(RandomizedBenchmarkingModel(), 10000, prior)

# As data arrives, the BCRB is given by:
# updater.current_bim


Ferrie and Granade, QIP 12 611 (2012). Granade et al, NJP 14 103013 (2012).

# Performance Results¶

SMC-accelerate algorithm, outperforms least-squares fitting, esp. with small amounts of data.

# Performance Results¶

This advantage persists for changing the maximum sequence length as well.

# Testing with Bad Prior and Physical Gates¶

To show that SMC acceleration is experimentally useful, we use a prior that is approximately 7 standard deviations away from the correct values for a cumulant-simulated gateset.

The data was simulated using the methods of Puzzuoli et al, PRA 89 022306.

## Results¶

Even with a significantly bad prior, SMC does quite well.

$$\begin{array}{l|cccc} & \tilde{p} & p_{\text{ref}} & A_0 & B_0 \\ \hline \text{True} & 0.9983 & 0.9957 & 0.3185 & 0.5012 \\ \text{SMC Estimate} & 0.9940 & 0.9968 & 0.3071 & 0.5134 \\ \text{LSF Estimate} & 0.9947 & 0.9972 & 0.3369 & 0.4820 \\ \hline \text{SMC Error} & 0.0043 & 0.0011 & 0.0113 & 0.0122 \\ \text{LSF Error} & 0.0036 & 0.0015 & 0.0184 & 0.0192 \end{array}$$

Due to the bad prior, it doesn't outperform least-squares fitting in this case for $\tilde{p}$, but it does very well at $p_{\text{ref}}$, $A$ and $B$, lending credibility to the estimate.

# Software Implementation¶

We have developed a flexible and easy-to-use Python library, QInfer, for implementing SMC-based applications.

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iframe("http://python-qinfer.readthedocs.org/en/latest/")

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# Conclusions¶

• Randomized benchmarking allows for efficiently extracting useful information about a channel.
• Bayesian inference dramatically reduces the data required for randomized benchmarking.
• SMC produces nearly optimal estimates.
• Our method is robust to bad priors, and works on physically-reasonable models.

# References¶

Full reference information is available on Zotero.

In [7]:
iframe('https://www.zotero.org/cgranade/items/collectionKey/2NQVPRK9')

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