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)
import qutip as qt
import benchmarkutils
name = "multiplication_bra_dense"
samples = 2
evals = 5
cutoffs = range(50, 501, 50)
def setup(N):
op1 = qt.rand_dm(N, N) * 0.2j
psi = qt.rand_ket(N).dag().full().ravel()
return op1, psi
def f(op1, psi):
return psi * op1
print("Benchmarking:", name)
print("Cutoff: ", end="", flush=True)
results = []
for N in cutoffs:
print(N, "", end="", flush=True)
op1, psi = setup(N)
t = benchmarkutils.run_benchmark(f, op1, psi, samples=samples, evals=evals)
results.append({"N": N, "t": t})
print()
benchmarkutils.save(name, results)
def setup(N, s):
op1 = sp.rand(N, N, 1., dtype=float) * 0.2j
op2 = sp.rand(N, N, s, dtype=float) * 0.1j
return op1, op2
def f(op1, op2):
return op1 + op2
for s in S:
name = basename + "_" + str(s).replace(".", "")
print("Benchmarking:", name)
print("Cutoff: ", end="", flush=True)
results = []
for N in cutoffs:
print(N, "", end="", flush=True)
T = 0.
for i in range(Nrand):
op1, op2 = setup(N, s)
T += benchmarkutils.run_benchmark(f,
op1,
op2,
samples=samples,
evals=evals)
results.append({"N": N, "t": T / Nrand})
print()
benchmarkutils.save(name, results)
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)
name = "displace"
samples = 3
evals = 10
cutoffs = range(10, 151, 10)
def setup(N):
alpha = np.log(N)
return alpha
def f(N, alpha):
return qt.displace(N, alpha)
print("Benchmarking:", name)
print("Cutoff: ", end="", flush=True)
checks = {}
results = []
for N in cutoffs:
print(N, "", end="", flush=True)
alpha = setup(N)
checks[N] = qt.expect(qt.destroy(N), f(N, alpha))
t = benchmarkutils.run_benchmark(f, N, alpha, samples=samples, evals=evals)
results.append({"N": N, "t": t})
print()
benchmarkutils.check(name, checks, 0.05)
benchmarkutils.save(name, results)
yvec = np.linspace(-50, 50, 100)
state = qt.coherent(N, alpha)
op = state * state.dag()
return op, xvec, yvec
def f(state, xvec, yvec):
return qt.wigner(state, xvec, yvec)
print("Benchmarking:", name)
print("Cutoff: ", end="", flush=True)
checks = {}
results = []
for N in cutoffs:
print(N, "", end="", flush=True)
op, xvec, yvec = setup(N)
alpha_check = 0.6 + 0.1j
checks[N] = f(op, [alpha_check.real], [alpha_check.imag])[0, 0]
t = benchmarkutils.run_benchmark(f,
op,
xvec,
yvec,
samples=samples,
evals=evals)
results.append({"N": N, "t": t})
print()
benchmarkutils.check(name, checks)
benchmarkutils.save(name, results)
name = "expect_operator"
samples = 5
evals = 100
cutoffs = range(100, 2501, 100)
def setup(N):
op = (qt.destroy(N) + qt.create(N))
psi = qt.Qobj(np.ones(N, complex)/(N**(1/2)))
rho = psi*psi.dag()
return op, rho
def f(op, rho):
return qt.expect(op, rho)
print("Benchmarking:", name)
print("Cutoff: ", end="", flush=True)
checks = {}
results = []
for N in cutoffs:
print(N, "", end="", flush=True)
op, rho = setup(N)
checks[N] = f(op, rho)
t = benchmarkutils.run_benchmark(f, op, rho, samples=samples, evals=evals)
results.append({"N": N, "t": t})
print()
benchmarkutils.check(name, checks)
benchmarkutils.save(name, results)
