# QuTiP lecture: Single-Atom-Lasing¶

Author: J.R. Johansson, [email protected]

http://dml.riken.jp/~rob/

In [1]:
# setup the matplotlib graphics library and configure it to show
# figures inline in the notebook
%pylab inline

Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].

In [2]:
# make qutip available in the rest of the notebook
from qutip import *


# Introduction and model¶

Consider a single atom coupled to a single cavity mode. If there atom excitation rate exceeds the relaxation rate, a population inversion can occur in the atom, and if coupled to the cavity the atom can then act as a photon pump on the cavity.

The coherent dynamics in this model is described by the Hamiltonian

$H = \hbar \omega_0 a^\dagger a + \frac{1}{2}\hbar\omega_a\sigma_z + \hbar g\sigma_x(a^\dagger + a)$

where $\omega_0$ is the cavity energy splitting, $\omega_a$ is the atom energy splitting and $g$ is the atom-cavity interaction strength.

In addition to the coherent dynamics the following incoherent processes are also present: 1) $\kappa$ relaxation and thermal excitations of the cavity, $\Gamma$ atomic excitation rate (pumping process).

The Lindblad master equation for the model is:

$\frac{d}{dt}\rho = -i[H, \rho] + \Gamma\left(\sigma_+\rho\sigma_- - \frac{1}{2}\sigma_-\sigma_+\rho - \frac{1}{2}\rho\sigma_-\sigma_+\right)$ $+ \kappa (1 + n_{\rm th}) \left(a\rho a^\dagger - \frac{1}{2}a^\dagger a\rho - \frac{1}{2}\rho a^\dagger a\right)$ $+ \kappa n_{\rm th} \left(a^\dagger\rho a - \frac{1}{2}a a^\dagger \rho - \frac{1}{2}\rho a a^\dagger\right)$

in units where $\hbar = 1$.

References:

### Problem parameters¶

In [3]:
w0 = 1.0  * 2 * pi  # cavity frequency
wa = 1.0  * 2 * pi  # atom frequency
g  = 0.05 * 2 * pi  # coupling strength

kappa = 0.04        # cavity dissipation rate
gamma = 0.00        # atom dissipation rate
Gamma = 0.35        # atom pump rate

N = 50              # number of cavity fock states
n_th_a = 0.0        # avg number of thermal bath excitation

tlist = linspace(0, 150, 101)


### Setup the operators, the Hamiltonian and initial state¶

In [4]:
# intial state

# operators
a  = tensor(destroy(N), qeye(2))
sm = tensor(qeye(N), destroy(2))
sx = tensor(qeye(N), sigmax())

# Hamiltonian
H = w0 * a.dag() * a + wa * sm.dag() * sm + g * (a.dag() + a) * sx

In [5]:
H

Out[5]:
$$\text{Quantum object: dims = [[50, 2], [50, 2]], shape = [100, 100], type = oper, isHerm = True}\\[1em]\begin{pmatrix}0.0 & 0.0 & 0.0 & 0.314159265359 & 0.0 & \cdots & 0.0 & 0.0 & 0.0 & 0.0 & 0.0\\0.0 & 6.28318530718 & 0.314159265359 & 0.0 & 0.0 & \cdots & 0.0 & 0.0 & 0.0 & 0.0 & 0.0\\0.0 & 0.314159265359 & 6.28318530718 & 0.0 & 0.0 & \cdots & 0.0 & 0.0 & 0.0 & 0.0 & 0.0\\0.314159265359 & 0.0 & 0.0 & 12.5663706144 & 0.444288293816 & \cdots & 0.0 & 0.0 & 0.0 & 0.0 & 0.0\\0.0 & 0.0 & 0.0 & 0.444288293816 & 12.5663706144 & \cdots & 0.0 & 0.0 & 0.0 & 0.0 & 0.0\\\vdots & \vdots & \vdots & \vdots & \vdots & \ddots & \vdots & \vdots & \vdots & \vdots & \vdots\\0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \cdots & 301.592894745 & 2.17655923708 & 0.0 & 0.0 & 0.0\\0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \cdots & 2.17655923708 & 301.592894745 & 0.0 & 0.0 & 2.19911485751\\0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \cdots & 0.0 & 0.0 & 307.876080052 & 2.19911485751 & 0.0\\0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \cdots & 0.0 & 0.0 & 2.19911485751 & 307.876080052 & 0.0\\0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \cdots & 0.0 & 2.19911485751 & 0.0 & 0.0 & 314.159265359\\\end{pmatrix}$$

### Create a list of collapse operators that describe the dissipation¶

In [6]:
# collapse operators
c_ops = []

rate = kappa * (1 + n_th_a)
if rate > 0.0:
c_ops.append(sqrt(rate) * a)

rate = kappa * n_th_a
if rate > 0.0:
c_ops.append(sqrt(rate) * a.dag())

rate = gamma
if rate > 0.0:
c_ops.append(sqrt(rate) * sm)

rate = Gamma
if rate > 0.0:
c_ops.append(sqrt(rate) * sm.dag())


### Evolve the system¶

Here we evolve the system with the Lindblad master equation solver, and we request that the expectation values of the operators $a^\dagger a$ and $\sigma_+\sigma_-$ are returned by the solver by passing the list [a.dag()*a, sm.dag()*sm] as the fifth argument to the solver.

In [7]:
opt = Odeoptions(nsteps=2000) # allow extra time-steps
output = mesolve(H, psi0, tlist, c_ops, [a.dag() * a, sm.dag() * sm], options=opt)


## Visualize the results¶

Here we plot the excitation probabilities of the cavity and the atom (these expectation values were calculated by the mesolve above).

In [8]:
n_c = output.expect[0]
n_a = output.expect[1]

fig, axes = subplots(1, 1, sharex=True, figsize=(12,8))

axes.plot(tlist, n_c, label="Cavity")
axes.plot(tlist, n_a, label="Atom excited state")
axes.set_xlim(0, 150)
axes.legend(loc=0)
axes.set_xlabel('Time')
axes.set_ylabel('Occupation probability')

Out[8]:
<matplotlib.text.Text at 0x2e08e50>

## Steady state: cavity fock-state distribution and wigner function¶

In [9]:
rho_ss = steadystate(H, c_ops)

In [10]:
fig, axes = subplots(1, 2, figsize=(16,6))

xvec = linspace(-5,5,200)

rho_cavity = ptrace(rho_ss, 0)
W = wigner(rho_cavity, xvec, xvec)
wlim = abs(W).max()

axes[1].contourf(xvec, xvec, W, 100, norm=mpl.colors.Normalize(-wlim,wlim), cmap=get_cmap('RdBu'))
axes[1].set_xlabel(r'Im $\alpha$', fontsize=18)
axes[1].set_ylabel(r'Re $\alpha$', fontsize=18)

axes[0].bar(arange(0, N), real(rho_cavity.diag()), color="blue", alpha=0.6)
axes[0].set_ylim(0, 1)
axes[0].set_xlim(0, N)
axes[0].set_xlabel('Fock number', fontsize=18)
axes[0].set_ylabel('Occupation probability', fontsize=18)

Out[10]:
<matplotlib.text.Text at 0x3273b50>

## Cavity fock-state distribution and Wigner function as a function of time¶

In [11]:
opt = Odeoptions(nsteps=5000) # allow extra time-steps
output = mesolve(H, psi0, linspace(0, 25, 5), c_ops, [], options=opt)

In [22]:
rho_ss_sublist = output.states

xvec = linspace(-5,5,200)

fig, axes = subplots(2, len(rho_ss_sublist), figsize=(3*len(rho_ss_sublist), 6))

for idx, rho_ss in enumerate(rho_ss_sublist):

# trace out the cavity density matrix
rho_ss_cavity = ptrace(rho_ss, 0)

# calculate its wigner function
W = wigner(rho_ss_cavity, xvec, xvec)

# plot its wigner function
wlim = abs(W).max()
axes[0,idx].contourf(xvec, xvec, W, 100, norm=mpl.colors.Normalize(-wlim,wlim), cmap=get_cmap('RdBu'))

# plot its fock-state distribution
axes[1,idx].bar(arange(0, N), real(rho_ss_cavity.diag()), color="blue", alpha=0.8)
axes[1,idx].set_ylim(0, 1)
axes[1,idx].set_xlim(0, 15)


## Steady state average photon occupation in cavity as a function of pump rate¶

References:

In [13]:
def calulcate_avg_photons(N, Gamma):

# collapse operators
c_ops = []

rate = kappa * (1 + n_th_a)
if rate > 0.0:
c_ops.append(sqrt(rate) * a)

rate = kappa * n_th_a
if rate > 0.0:
c_ops.append(sqrt(rate) * a.dag())

rate = gamma
if rate > 0.0:
c_ops.append(sqrt(rate) * sm)

rate = Gamma
if rate > 0.0:
c_ops.append(sqrt(rate) * sm.dag())

# Ground state and steady state for the Hamiltonian: H = H0 + g * H1

n_cavity = expect(a.dag() * a, rho_ss)

return n_cavity

In [14]:
Gamma_max = 2 * (4*g**2) / kappa
Gamma_vec = linspace(0.0, Gamma_max, 50)

n_avg_vec = [calulcate_avg_photons(N, Gamma) for Gamma in Gamma_vec]

In [15]:
fig, axes = subplots(1, 1, figsize=(12,6))

axes.plot(Gamma_vec * kappa / (4*g**2), n_avg_vec, color="blue", alpha=0.6, label="numerical")

axes.set_xlabel(r'$\Gamma\kappa/(4g^2)$', fontsize=18)
axes.set_ylabel(r'Occupation probability $\langle n \rangle$', fontsize=18)
axes.legend()

Out[15]:
<matplotlib.legend.Legend at 0x616dc10>

Here we see that lasing is suppressed for $\Gamma\kappa/(4g^2) > 1$.

Let's look at the fock-state distribution at $\Gamma\kappa/(4g^2) = 0.5$ (lasing regime) and $\Gamma\kappa/(4g^2) = 1.5$ (suppressed regime):

### Case 1: $\Gamma\kappa/(4g^2) = 0.5$¶

In [16]:
Gamma = 0.5 * (4*g**2) / kappa

In [17]:
c_ops = [sqrt(kappa * (1 + n_th_a)) * a, sqrt(kappa * n_th_a) * a.dag(), sqrt(gamma) * sm, sqrt(Gamma) * sm.dag()]


In [18]:
fig, axes = subplots(1, 2, figsize=(16,6))

xvec = linspace(-10,10,200)

rho_cavity = ptrace(rho_ss, 0)
W = wigner(rho_cavity, xvec, xvec)
wlim = abs(W).max()
axes[1].contourf(xvec, xvec, W, 100, norm=mpl.colors.Normalize(-wlim,wlim), cmap=get_cmap('RdBu'))

axes[0].bar(arange(0, N), real(rho_cavity.diag()), color="blue", alpha=0.6)
axes[0].set_ylim(0, 1)
axes[0].set_xlim(0, N)

Out[18]:
(0, 50)

### Case 2: $\Gamma\kappa/(4g^2) = 1.5$¶

In [19]:
Gamma = 1.5 * (4*g**2) / kappa

In [20]:
c_ops = [sqrt(kappa * (1 + n_th_a)) * a, sqrt(kappa * n_th_a) * a.dag(), sqrt(gamma) * sm, sqrt(Gamma) * sm.dag()]


In [21]:
fig, axes = subplots(1, 2, figsize=(16,6))

xvec = linspace(-10,10,200)

rho_cavity = ptrace(rho_ss, 0)
W = wigner(rho_cavity, xvec, xvec)
wlim = abs(W).max()
axes[1].contourf(xvec, xvec, W, 100, norm=mpl.colors.Normalize(-wlim,wlim), cmap=get_cmap('RdBu'))

axes[0].bar(arange(0, N), real(rho_cavity.diag()), color="blue", alpha=0.6)
axes[0].set_ylim(0, 1)
axes[0].set_xlim(0, N)

Out[21]:
(0, 50)

Too large pumping rate $\Gamma$ kills the lasing process: reversed threshold.