def measure_fock(self, modes, shots=1, select=None, **kwargs):
if select is not None:
raise NotImplementedError(
"Gaussian backend currently does not support "
"postselection")
if shots != 1:
warnings.warn(
"Cannot simulate non-Gaussian states. "
"Conditional state after Fock measurement has not been updated."
)
mu = self.circuit.mean
mean = self.circuit.smeanxp()
cov = self.circuit.scovmatxp()
x_idxs = array(modes)
p_idxs = x_idxs + len(mu)
modes_idxs = concatenate([x_idxs, p_idxs])
reduced_cov = cov[ix_(modes_idxs, modes_idxs)]
reduced_mean = mean[modes_idxs]
# check we are sampling from a gaussian state with zero mean
if allclose(mu, zeros_like(mu)):
samples = hafnian_sample_state(reduced_cov, shots)
else:
samples = hafnian_sample_state(reduced_cov,
shots,
mean=reduced_mean)
return samples
def measure_fock(self, modes, shots=1, select=None):
if shots != 1:
if select is not None:
raise NotImplementedError(
"Gaussian backend currently does not support "
"postselection")
warnings.warn(
"Cannot simulate non-Gaussian states. "
"Conditional state after Fock measurement has not been updated."
)
mu = self.circuit.mean
mean = self.circuit.smeanxp()
cov = self.circuit.scovmatxp()
x_idxs = array(modes)
p_idxs = x_idxs + len(mu)
modes_idxs = concatenate([x_idxs, p_idxs])
reduced_cov = cov[ix_(modes_idxs, modes_idxs)]
reduced_mean = mean[modes_idxs]
# check we are sampling from a gaussian state with zero mean
if allclose(mu, zeros_like(mu)):
samples = hafnian_sample_state(reduced_cov, shots)
else:
samples = hafnian_sample_state(reduced_cov,
shots,
mean=reduced_mean)
# for backward compatibility with previous measurement behaviour,
# if only one shot, then we drop the shots axis
if shots == 1:
samples = samples.reshape((len(modes), ))
return samples
def test_seed():
"""Tests that seed method does reset the random number generators"""
n_samples = 10
mean_n = 1.0
r = np.arcsinh(np.sqrt(mean_n))
V = np.array([[np.exp(2 * r), 0.0], [0.0, np.exp(-2 * r)]])
seed(137)
first_sample = hafnian_sample_state(V, n_samples)
second_sample = hafnian_sample_state(V, n_samples)
seed(137)
first_sample_p = hafnian_sample_state(V, n_samples)
second_sample_p = hafnian_sample_state(V, n_samples)
assert np.array_equal(first_sample, first_sample_p)
assert np.array_equal(second_sample, second_sample_p)
def test_single_squeezed_state_hafnian(self):
"""Test the sampling routines by comparing the photon number frequencies and the exact
probability distribution of a single mode squeezed vacuum state
"""
n_samples = 1000
mean_n = 1.0
r = np.arcsinh(np.sqrt(mean_n))
sigma = np.array([[np.exp(2 * r), 0.0], [0.0, np.exp(-2 * r)]])
n_cut = 10
samples = hafnian_sample_state(sigma, samples=n_samples, cutoff=n_cut)
bins = np.arange(0, max(samples) + 1, 1)
(freq, _) = np.histogram(samples, bins=bins)
rel_freq = freq / n_samples
nm = max(samples) // 2
x = nbinom.pmf(np.arange(0, nm, 1), 0.5,
np.tanh(np.arcsinh(np.sqrt(mean_n)))**2)
x2 = np.zeros(2 * len(x))
x2[::2] = x
rel_freq = freq[0:-1] / n_samples
x2 = x2[0:len(rel_freq)]
assert np.allclose(rel_freq,
x2,
atol=rel_tol / np.sqrt(n_samples),
rtol=rel_tol / np.sqrt(n_samples))
def test_TMS_hafnian_sample_states_cutoff(self):
"""test sampling from TMS hafnians is correlated"""
m = 0.432
phi = 0.546
V = TMS_cov(np.arcsinh(m), phi)
res = hafnian_sample_state(V, samples=20, cutoff=5)
assert np.allclose(res[:, 0], res[:, 1])
def test_hafnian_state_lowmax(self, max_photons):
"""test sampling never exceeds max photons for hafnian"""
m = 0.432
phi = 0.546
V = TMS_cov(np.arcsinh(m), phi)
res = hafnian_sample_state(V, samples=10, max_photons=max_photons)
assert np.max(res) <= max_photons
def test_displaced_two_mode_squeezed_state_hafnian(self):
"""Test the sampling routines by comparing the photon number frequencies and the exact
probability distribution of a displaced two mode squeezed vacuum state
"""
n_samples = 1000
n_cut = 10
mean_n = 1
r = np.arcsinh(np.sqrt(mean_n))
c = np.cosh(2 * r)
s = np.sinh(2 * r)
sigma = np.array([[c, s, 0, 0], [s, c, 0, 0], [0, 0, c, -s],
[0, 0, -s, c]])
mean = 2 * np.array([0.1, 0.25, 0.1, 0.25])
samples = hafnian_sample_state(sigma,
samples=n_samples,
mean=mean,
cutoff=n_cut)
probs = np.real_if_close(
np.array([[
density_matrix_element(mean, sigma, [i, j], [i, j])
for i in range(n_cut)
] for j in range(n_cut)]))
freq, _, _ = np.histogram2d(samples[:, 1],
samples[:, 0],
bins=np.arange(0, n_cut + 1))
rel_freq = freq / n_samples
assert np.allclose(rel_freq,
probs,
rtol=rel_tol / np.sqrt(n_samples),
atol=rel_tol / np.sqrt(n_samples))
def test_two_mode_squeezed_state_hafnian(self):
"""Test the sampling routines by comparing the photon number frequencies and the exact
probability distribution of a two mode squeezed vacuum state
"""
n_samples = 1000
n_cut = 5
mean_n = 1.0
r = np.arcsinh(np.sqrt(mean_n))
c = np.cosh(2 * r)
s = np.sinh(2 * r)
sigma = np.array([[c, s, 0, 0], [s, c, 0, 0], [0, 0, c, -s],
[0, 0, -s, c]])
samples = hafnian_sample_state(sigma, samples=n_samples, cutoff=n_cut)
assert np.all(samples[:, 0] == samples[:, 1])
samples1d = samples[:, 0]
bins = np.arange(0, max(samples1d) + 1, 1)
(freq, _) = np.histogram(samples1d, bins=bins)
rel_freq = freq / n_samples
probs = (1.0 / (1.0 + mean_n)) * (mean_n / (1.0 + mean_n))**bins[0:-1]
probs[-1] = 1.0 - np.sum(
probs[0:-1]
) # Coarse grain all the probabilities past the threshold
assert np.allclose(rel_freq,
probs,
atol=rel_tol / np.sqrt(n_samples),
rtol=rel_tol / np.sqrt(n_samples))
def test_hafnian_sample_states_nans(self):
"""test exception is raised if not a numpy array"""
with pytest.raises(ValueError,
match="Covariance matrix must not contain NaNs."):
hafnian_sample_state(np.array([[0, 5], [0, np.NaN]]), samples=20)
def test_hafnian_sample_states_nonsquare(self):
"""test exception is raised if not a numpy array"""
with pytest.raises(ValueError,
match="Covariance matrix must be square."):
hafnian_sample_state(np.array([[0, 5, 3], [0, 1, 2]]), samples=20)
def test_hafnian_sample_states_nonnumpy(self):
"""test exception is raised if not a numpy array"""
with pytest.raises(TypeError):
hafnian_sample_state(5, samples=20)
