def test_two_mode_squeezed_state_torontonian(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
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 = torontonian_sample_state(sigma, samples=n_samples)
assert np.all(samples[:, 0] == samples[:, 1])
samples1d = samples[:, 0]
bins = np.arange(0, max(samples1d), 1)
(freq, _) = np.histogram(samples1d, bins=bins)
rel_freq = freq / n_samples
probs = np.empty([2])
probs[0] = 1.0 / (1.0 + mean_n)
probs[1] = 1.0 - probs[0]
assert np.allclose(
rel_freq,
probs[0:-1],
atol=rel_tol / np.sqrt(n_samples),
rtol=rel_tol / np.sqrt(n_samples),
)
def measure_threshold(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 Threshold measurement has not been updated."
)
mu = self.circuit.mean
cov = self.circuit.scovmatxp()
# check we are sampling from a gaussian state with zero mean
if not allclose(mu, zeros_like(mu)):
raise NotImplementedError(
"Threshold measurement is only supported for "
"Gaussian states with zero mean")
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)]
samples = torontonian_sample_state(reduced_cov, shots)
# 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_torontonian_state_lowmax(self, max_photons):
"""test sampling never exceeds max photons for torontonian"""
m = 0.432
phi = 0.546
V = TMS_cov(np.arcsinh(m), phi)
res = torontonian_sample_state(V, samples=10, max_photons=max_photons)
assert np.max(res) <= max_photons
def measure_threshold(self, modes, shots=1, select=None, **kwargs):
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 Threshold measurement has not been updated."
)
mu = self.circuit.mean
cov = self.circuit.scovmatxp()
mean = self.circuit.smeanxp()
# check we are sampling from a gaussian state with zero mean
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]
samples = torontonian_sample_state(mu=reduced_mean,
cov=reduced_cov,
samples=shots)
return samples
def test_single_coherent_state_torontonian(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 = 10000
sigma = np.array([[1.0, 0.0], [0.0, 1.0]])
mu = np.array([1.0, 1.0])
samples = torontonian_sample_state(sigma, samples=n_samples, mu=mu)
samples_list = list(samples)
rel_freq = np.array([samples_list.count(0),
samples_list.count(1)]) / n_samples
x2 = np.array([np.exp(-0.5), 1 - np.exp(-0.5)])
assert np.allclose(rel_freq,
x2,
atol=rel_tol / np.sqrt(n_samples),
rtol=rel_tol / np.sqrt(n_samples))
def test_single_squeezed_state_torontonian(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 = 10000
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)]])
samples = torontonian_sample_state(sigma, samples=n_samples)
samples_list = list(samples)
rel_freq = np.array([samples_list.count(0),
samples_list.count(1)]) / n_samples
x2 = np.empty([2])
x2[0] = 1.0 / np.sqrt(1.0 + mean_n)
x2[1] = 1.0 - x2[0]
assert np.allclose(rel_freq,
x2,
atol=rel_tol / np.sqrt(n_samples),
rtol=rel_tol / np.sqrt(n_samples))
def test_torontonian_samples_nans(self):
"""test exception is raised if not a numpy array"""
with pytest.raises(ValueError,
match="Covariance matrix must not contain NaNs."):
torontonian_sample_state(np.array([[0, 5], [0, np.NaN]]),
samples=20)
def test_torontonian_samples_nonsquare(self):
"""test exception is raised if not a numpy array"""
with pytest.raises(ValueError,
match="Covariance matrix must be square."):
torontonian_sample_state(np.array([[0, 5, 3], [0, 1, 2]]),
samples=20)
def test_torontonian_samples_nonnumpy(self):
"""test exception is raised if not a numpy array"""
with pytest.raises(TypeError):
torontonian_sample_state(5, samples=20)