J = [x + 1j * p]
options = qt.Options()
options.nsteps = 1000000
options.atol = 1e-8
options.rtol = 1e-6
return psi0, H, x, J, options
def f(psi0, H, x, J, options):
tlist = np.linspace(0, 10, 11)
exp_x = qt.mesolve(H, psi0, tlist, J, [x], options=options).expect[0]
return exp_x
print("Benchmarking:", name)
print("Cutoff: ", end="", flush=True)
checks = {}
results = []
for N in cutoffs:
print(N, "", end="", flush=True)
psi0, H, x, J, options = setup(N)
checks[N] = sum(f(psi0, H, x, J, options))
t = benchmarkutils.run_benchmark(f, psi0, H, x, J, options, samples=samples, evals=evals)
results.append({"N": N, "t": t})
print()
benchmarkutils.check(name, checks, 1e-4)
benchmarkutils.save(name, results)
alpha0 = 0.3 - 0.5j
tspan = np.linspace(0, 10, 11)
a = qt.destroy(N)
at = qt.create(N)
n = at * a
H = delta_c * n + eta * (a + at)
J = [np.sqrt(kappa) * a]
psi0 = qt.coherent(N, alpha0)
exp_n = qt.mcsolve(H, psi0, tspan, J, [n], ntraj=evals,
options=options).expect[0]
return np.real(exp_n)
print("Benchmarking:", name)
print("Cutoff: ", end="", flush=True)
checks = {}
results = []
for N in cutoffs:
print(N, "", end="", flush=True)
options = setup(N)
checks[N] = sum(f(N, options))
t = benchmarkutils.run_benchmark(f, N, options, samples=samples, evals=1)
results.append({"N": N, "t": t / evals})
print()
benchmarkutils.check(name, checks, eps=0.05)
benchmarkutils.save(name, results)
np.sqrt(gamma) * sm),
]
psi0 = qt.tensor(qt.fock(N, 0), (qt.basis(2, 0) + qt.basis(2, 1)).unit())
exp_n = qt.mesolve(H,
psi0,
tspan,
c_ops, [qt.tensor(n, Ia)],
options=options).expect[0]
return exp_n
print("Benchmarking:", name)
print("Cutoff: ", end="", flush=True)
checks = {}
results = []
for N in cutoffs:
print(N, "", end="", flush=True)
options = setup(N)
checks[N] = sum(f(N, options))
t = benchmarkutils.run_benchmark(f,
N,
options,
samples=samples,
evals=evals)
results.append({"N": 2 * N, "t": t})
print()
benchmarkutils.check(name, checks)
benchmarkutils.save(name, results)
