def test_bad_event_photon_numbers(self):
"""Test if function raises a ``ValueError`` when input a minimum photon number that is
below zero."""
with pytest.raises(
ValueError,
match="Cannot request events with photon number below zero"):
similarity.feature_vector_sampling([[1, 1, 0], [1, 0, 1]], [-1, 4],
1)
def test_bad_max_count(self):
"""Test if function raises a ``ValueError`` when input a non-positive value for the
maximum photon count per mode."""
with pytest.raises(
ValueError,
match="Maximum number of photons per mode must be at least"):
similarity.feature_vector_sampling([[1, 1, 0], [1, 0, 1]], [2, 4],
0)
def test_correct_distribution(self, monkeypatch):
"""Test if function correctly constructs the feature vector corresponding to some hard
coded samples. This test uses a set of samples, corresponding events, and resultant
feature vector to test against the output of ``feature_vector_sampling``. The
``sample_to_event`` function called within ``feature_vector_sampling`` is monkeypatched
to return the hard coded events corresponding to the samples."""
samples_events_mapping = { # max_count_per_mode = 1
(1, 1, 0, 0, 0): 2,
(1, 1, 1, 0, 0): 3,
(1, 1, 1, 1, 0): 4,
(1, 1, 1, 1, 1): 5,
(2, 0, 0, 0, 0): None,
(3, 0, 0, 0, 0): None,
(4, 0, 0, 0, 0): None,
(5, 0, 0, 0, 0): None,
(0, 1, 1, 0, 0): 2,
}
samples = list(samples_events_mapping.keys()) + [(1, 1, 1, 1, 1)
] # add a repetition
event_photon_numbers = [2, 1, 3, 5] # test alternative ordering
fv_true = [0.2, 0, 0.1, 0.2]
with monkeypatch.context() as m:
m.setattr(similarity, "sample_to_event",
lambda x, _: samples_events_mapping[x])
fv = similarity.feature_vector_sampling(samples,
event_photon_numbers, 1)
assert fv_true == fv
# Calculating a feature vector
# ----------------------------
#
# We provide two methods for calculating a feature vector of GBS event probabilities in
# Strawberry Fields:
#
# 1. Through sampling.
# 2. Using a Monte Carlo estimate of the probability.
#
# In the first method, all one needs to do is generate some GBS samples from the graph of
# interest and fix the composition of the feature vector. For example, for a feature vector
# :math:`f_{\mathbf{k} = (2, 4, 6), n_{\max}=2}` we use:
print(
similarity.feature_vector_sampling(m0,
event_photon_numbers=[2, 4, 6],
max_count_per_mode=2))
##############################################################################
# For the second method, suppose we want to calculate the event probabilities exactly rather than
# through sampling. To do this, we consider the event probability :math:`p_{k, n_{\max}}` as the
# sum over all sample probabilities in the event. In GBS, each sample probability is determined by
# the hafnian of a relevant sub-adjacency matrix. While this is tough to calculate, what makes
# calculating :math:`p_{k, n_{\max}}` really challenging is the number of samples the corresponding
# event contains! For example, the 6-photon event over 17 modes :math:`E_{k=6, n_{\max}=2}`
# contains the following number of samples :
print(similarity.event_cardinality(6, 2, 17))
##############################################################################
# To avoid calculating a large number of sample probabilities, an alternative is to perform a
def test_low_count(self):
"""Test if function raises a ``ValueError`` if ``max_count_per_mode`` is negative."""
with pytest.raises(ValueError, match="Maximum number of photons"):
similarity.feature_vector_sampling([[1, 1, 0], [1, 0, 1]], [2, 4],
-1)