def test_insufficient_samples(self):
"""Test if function returns ``ValueError`` when user specifies a number of samples less
than 1 """
with pytest.raises(
ValueError,
match="Number of samples must be greater than zero"):
sample.uniform_sampler(modes=2, sampled_modes=2, samples=0)
def test_output_dimensions(self):
"""Tests if function returns list of correct dimensions"""
samples = np.array(
sample.uniform_sampler(modes=2, sampled_modes=1, samples=3))
dims = samples.shape
assert len(dims) == 2
assert dims[0] == 3
assert dims[1] == 2
def test_random_seed(dim, adj):
"""Test for the function ``strawberryfields.gbs.sample.random_seed``. Checks that samples are identical
after repeated initialization of ``random_seed``."""
sample.random_seed(1968)
q_s_1 = sample.quantum_sampler(A=adj,
n_mean=2,
samples=10,
backend_options={"threshold": False})
u_s_1 = sample.uniform_sampler(modes=dim, sampled_modes=2, samples=10)
sample.random_seed(1968)
q_s_2 = sample.quantum_sampler(A=adj,
n_mean=2,
samples=10,
backend_options={"threshold": False})
u_s_2 = sample.uniform_sampler(modes=dim, sampled_modes=2, samples=10)
assert np.array_equal(q_s_1, q_s_2)
assert np.array_equal(u_s_1, u_s_2)
def test_output_sampled_modes(self):
"""Tests if samples from function are only zeros and ones and have a number of ones equal to
``sampled_modes``"""
samples = np.array(
sample.uniform_sampler(modes=2, sampled_modes=1, samples=3))
only_zero_one = True
for samp in samples:
if not np.allclose(np.unique(samp), [0, 1]):
only_zero_one = False
assert only_zero_one
assert np.unique(np.sum(samples, axis=1)) == 1
def test_sampled_modes_exceeds_modes(self):
"""Test if function returns ``ValueError`` when user specifies a number of
``sampled_modes`` that exceeds ``modes`` """
with pytest.raises(ValueError, match="Modes and sampled_modes"):
sample.uniform_sampler(modes=2, sampled_modes=3, samples=2)
def test_insufficient_sampled_modes(self):
"""Tests if function returns ``ValueError`` when user specifies a number of
``sampled_modes`` less than 1 """
with pytest.raises(ValueError, match="Modes and sampled_modes"):
sample.uniform_sampler(modes=2, sampled_modes=0, samples=2)
def sample_subgraphs(
graph: nx.Graph,
nodes: int,
samples: int = 1,
sample_options: Optional[dict] = None,
backend_options: Optional[dict] = None,
) -> list:
"""Functionality for sampling subgraphs.
The optional ``sample_options`` argument can be used to specify the type of sampling. It
should be a dict that contains any of the following:
.. glossary::
key: ``"distribution"``, value: *str*
Subgraphs can be sampled according to the following distributions:
- ``"gbs"``: for generating subgraphs according to the Gaussian boson sampling
distribution. In this distribution, subgraphs are sampled with a variable size (
default).
- ``"uniform"``: for generating subgraphs uniformly at random. When using this
distribution, subgraphs are of a fixed size. Note that ``backend_options`` are not
used and that remote sampling is unavailable due to the simplicity of the
distribution.
key: ``"postselect_ratio"``, value: *float*
Ratio of ``nodes`` used to determine the minimum size of subgraph sampled; defaults
to 0.75.
Args:
graph (nx.Graph): the input graph
nodes (int): the mean size of subgraph samples
samples (int): number of samples; defaults to 1
sample_options (dict[str, Any]): dictionary specifying options used by
:func:`sample_subgraphs`; defaults to :const:`SAMPLE_DEFAULTS`
backend_options (dict[str, Any]): dictionary specifying options used by backends during
sampling; defaults to ``None``
Returns:
list[list[int]]: a list of length ``samples`` whose elements are subgraphs given by a
list of nodes
"""
sample_options = {**SAMPLE_DEFAULTS, **(sample_options or {})}
distribution = sample_options["distribution"]
if distribution == "uniform":
s = sample.uniform_sampler(modes=graph.order(),
sampled_modes=nodes,
samples=samples)
elif distribution == "gbs":
postselect = int(sample_options["postselect_ratio"] * nodes)
backend_options = {**(backend_options or {}), "postselect": postselect}
s = sample.quantum_sampler(
A=nx.to_numpy_array(graph),
n_mean=nodes,
samples=samples,
backend_options=backend_options,
)
else:
raise ValueError("Invalid distribution selected")
return to_subgraphs(graph, s)