Notebook

Stochastic Solver: Photo-current detection in a JC model¶

In [1]:

import matplotlib.pyplot as plt
import numpy as np
from matplotlib import rcParams
from qutip import (Options, about, destroy, fock, identity, mesolve,
                   parallel_map, photocurrent_mesolve, tensor)

%matplotlib inline
rcParams["font.family"] = "STIXGeneral"
rcParams["mathtext.fontset"] = "stix"
rcParams["font.size"] = "14"

In [2]:

N = 15
w0 = 1.0 * 2 * np.pi
g = 0.2 * 2 * np.pi
times = np.linspace(0, 15, 150)
dt = times[1] - times[0]
gamma = 0.01
kappa = 0.1
ntraj = 150

In [3]:

a = tensor(destroy(N), identity(2))
sm = tensor(identity(N), destroy(2))

In [4]:

H = w0 * a.dag() * a + w0 * sm.dag() * sm + g * (sm * a.dag() + sm.dag() * a)

In [5]:

rho0 = tensor(fock(N, 5), fock(2, 0))

In [6]:

e_ops = [a.dag() * a, a + a.dag(), sm.dag() * sm]

Highly efficient detection¶

In [7]:

c_ops = [np.sqrt(gamma) * sm]  # collapse operator for qubit
sc_ops = [np.sqrt(kappa) * a]  # stochastic collapse for resonator

In [8]:

result_ref = mesolve(H, rho0, times, c_ops + sc_ops, e_ops)

In [9]:

result1 = photocurrent_mesolve(
    H,
    rho0,
    times,
    c_ops=c_ops,
    sc_ops=sc_ops,
    e_ops=e_ops,
    ntraj=1,
    nsubsteps=100,
    store_measurement=True,
    options=Options(store_states=True),
)

Total run time:   0.35s

Run the smesolve solver in parallel by passing the keyword argument map_func=parallel_map:

In [10]:

result2 = photocurrent_mesolve(
    H,
    rho0,
    times,
    c_ops=c_ops,
    sc_ops=sc_ops,
    e_ops=e_ops,
    ntraj=ntraj,
    nsubsteps=100,
    store_measurement=True,
    options=Options(store_states=True),
    map_func=parallel_map,
)

10.0%. Run time:   3.00s. Est. time left: 00:00:00:27
20.0%. Run time:   5.67s. Est. time left: 00:00:00:22
30.0%. Run time:   8.92s. Est. time left: 00:00:00:20
40.0%. Run time:  11.65s. Est. time left: 00:00:00:17
50.0%. Run time:  14.50s. Est. time left: 00:00:00:14
60.0%. Run time:  17.30s. Est. time left: 00:00:00:11
70.0%. Run time:  20.07s. Est. time left: 00:00:00:08
80.0%. Run time:  22.98s. Est. time left: 00:00:00:05
90.0%. Run time:  25.74s. Est. time left: 00:00:00:02
100.0%. Run time:  28.60s. Est. time left: 00:00:00:00
Total run time:  28.61s

# alternative: use the parallel_map based on IPython.parallel from qutip.ipynbtools import parallel_map as ip_parallel_map result2 = smesolve(H, rho0, times, c_ops=c_ops, sc_ops=sc_ops, e_ops=e_ops, ntraj=ntraj, nsubsteps=100, method='photocurrent', store_measurement=True, options=Options(store_states=True), progress_bar=HTMLProgressBar(), map_func=ip_parallel_map)

In [11]:

fig, axes = plt.subplots(2, 3, figsize=(16, 8), sharex=True)

axes[0, 0].plot(times,
                result1.expect[0], label=r"Stochastic ME (ntraj = 1)", lw=2)
axes[0, 0].plot(times, result_ref.expect[0], label=r"Lindblad ME", lw=2)
axes[0, 0].set_title("Cavity photon number (ntraj = 1)")
axes[0, 0].legend()

axes[1, 0].plot(
    times, result2.expect[0], label=r"Stochatic ME (ntraj = %d)" % ntraj, lw=2
)
axes[1, 0].plot(times, result_ref.expect[0], label=r"Lindblad ME", lw=2)
axes[1, 0].set_title("Cavity photon number (ntraj = 10)")
axes[1, 0].legend()


axes[0, 1].plot(times,
                result1.expect[2], label=r"Stochastic ME (ntraj = 1)", lw=2)
axes[0, 1].plot(times, result_ref.expect[2], label=r"Lindblad ME", lw=2)
axes[0, 1].set_title("Qubit excition probability (ntraj = 1)")
axes[0, 1].legend()

axes[1, 1].plot(
    times, result2.expect[2], label=r"Stochatic ME (ntraj = %d)" % ntraj, lw=2
)
axes[1, 1].plot(times, result_ref.expect[2], label=r"Lindblad ME", lw=2)
axes[1, 1].set_title("Qubit excition probability (ntraj = %d)" % ntraj)
axes[1, 1].legend()


axes[0, 2].step(times, dt * np.cumsum(result1.measurement[0].real), lw=2)
axes[0, 2].set_title("Cummulative photon detections (ntraj = 1)")
axes[1, 2].step(
    times,
    dt * np.cumsum(np.array(result2.measurement).sum(axis=0).real) / ntraj,
    lw=2
)
axes[1, 2].set_title("Cummulative avg. photon detections (ntraj = %d)" % ntraj)

fig.tight_layout()

Versions¶

In [12]:

about()

QuTiP: Quantum Toolbox in Python
================================
Copyright (c) QuTiP team 2011 and later.
Current admin team: Alexander Pitchford, Nathan Shammah, Shahnawaz Ahmed, Neill Lambert, Eric Giguère, Boxi Li, Jake Lishman, Simon Cross and Asier Galicia.
Board members: Daniel Burgarth, Robert Johansson, Anton F. Kockum, Franco Nori and Will Zeng.
Original developers: R. J. Johansson & P. D. Nation.
Previous lead developers: Chris Granade & A. Grimsmo.
Currently developed through wide collaboration. See https://github.com/qutip for details.

QuTiP Version:      4.7.1
Numpy Version:      1.22.4
Scipy Version:      1.8.1
Cython Version:     0.29.33
Matplotlib Version: 3.5.2
Python Version:     3.10.4
Number of CPUs:     2
BLAS Info:          Generic
OPENMP Installed:   False
INTEL MKL Ext:      False
Platform Info:      Linux (x86_64)
Installation path:  /home/runner/work/qutip-tutorials/qutip-tutorials/qutip/qutip
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