# Source code for strawberryfields.apps.similarity

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r"""
Tools to construct graph kernels from GBS.

The graph kernel is built by mapping GBS samples from each graph to a feature vector. Similarity
between graphs can then be determined by calculating the overlap between these vectors.

The functionality here is based upon the research papers:
:cite:bradler2018graph,schuld2019quantum,bradler2019duality.

.. seealso::

:ref:apps-sim-tutorial

Coarse-graining GBS samples
---------------------------

GBS feature vectors can be composed of probabilities of coarse-grained combinations of elementary
samples. We consider two coarse grainings:

- **Orbits:** Combine all samples that can be made identical under permutation. Orbits are
written simply as a sorting of integer photon number samples in non-increasing order with the
zeros at the end removed. For example, [1, 1, 2, 0] and [2, 1, 0, 1] both belong to the
[2, 1, 1] orbit.

- **Events:** Combine all :math:k-photon orbits where the maximum photon count in any mode does
not exceed a fixed value :math:n_{\max} into an event :math:E_{k, n_{\max}}. For example,
orbits [2, 1], [1, 1, 1] are part of the :math:E_{k=3, n_{\max}=2} event, while
orbit [3] is not.

This module provides the following tools for dealing with coarse-grained orbits and events.

Creating a feature vector
-------------------------

A feature vector of a graph can be created by choosing a collection of orbits or events and
evaluating their probabilities with respect to GBS with the embedded graph. These
probabilities are then selected to be elements of the feature vector. For example, suppose event
probabilities :math:p_{k} := p_{E_{k, n_{\max}}} with all events :math:E_{k, n_{\max}}
having a maximum photon count :math:n_{\max} in each mode are collected. If :math:\mathbf{k}
is the vector of selected events, the resultant feature vector is

.. math::
f_{\mathbf{k}} = (p_{k_{1}}, p_{k_{2}}, \ldots).

Evaluating the probabilities of orbits or events can be achieved through three approaches:

- **Direct sampling:** infer the probability of orbits or events from a set of sample data.

- **Monte Carlo estimation:** generate samples within a given orbit or event and use them
to estimate the probability.

- **Exact calculation:** probabilities are calculated exactly, which involves calculating a
large number of hafnians.

In the direct sampling approach, :math:N samples are taken from GBS with the embedded graph.
For an event :math:E, for example, the number of samples that fall within this event
are counted, resulting in the count :math:c_{E}. The probability :math:p(E) of this event
is then approximated as:

.. math::
p(E) \approx \frac{c_{E}}{N}.

Calculating the probability of any orbit using direct sampling follows the same protocol.

To perform a Monte Carlo estimation of the probability of an event :math:E,
several samples from :math:E are drawn uniformly at random using :func:event_to_sample.
Suppose :math:N samples
:math:\{S_{1}, S_{2}, \ldots , S_{N}\} are generated. For each sample, this function calculates
the probability :math:p(S_i) of observing that sample from a GBS device programmed according to
the input graph and mean photon number. The sum of the probabilities is then rescaled according
to the cardinality :math:|E| and the total number of samples:

.. math::
p(E) \approx \frac{1}{N}\sum_{i=1}^N p(S_i) |E|.

The sample mean of this sum is an estimate of the rescaled probability :math:p(E). The same
protocol applies when estimating the probability of an orbit :math:O. This time, however,
:func:orbit_to_sample function is used to draw :math:N samples and calculate :math:p(O).

Calculating exact probabilities can be more expensive but provides the capability of doing
simulations without approximations, which might be important for benchmarking GBS similarity
applications. For example, exact feature vectors might be preferred in applications where the
approximations can drown out the differences in graphs. The exact probability of an orbit is
made up of the sum of probabilities of all possible GBS output patterns that belong to it:

.. math::
p(O) = \sum_{S \in O} p(S)

where :math:S represents a GBS output pattern. Calculating each :math:p(S) requires
computing a hafnian <https://the-walrus.readthedocs.io/en/latest/hafnian.html>__, which gets
exponentially difficult with increasing photon number. Exact probabilities of
events can be calculated by summing over their constituent orbit probabilities.

This module provides functions for feature vectors to be calculated using all the methods
listed above, i.e, direct sampling, Monte Carlo (MC) estimation and exact calculations. All
methods are also separately implemented for using either events or orbits as the constructing
unit of feature vectors. Functions :func:~.feature_vector_orbits_sampling and
:func:~.feature_vector_events_sampling calculate feature vectors using direct sampling and
require a list of pre-generated samples.

Functions :func:~.feature_vector_orbits and :func:~.feature_vector_events can be used to get
exact feature vectors. These functions use a keyword argument samples to signal producing
either exact or approximate probabilities. samples is set to None to get exact feature
vectors by default. To use Monte Carlo estimation, samples can be set to the number of samples
desired to be used in the estimation. Similar to the :func:~.apps.sample.sample function, these two
functions include a loss argument to specify the proportion of photons lost in the simulated GBS device.
"""
from collections import Counter
from typing import Generator, Union

import networkx as nx
import numpy as np
from scipy.special import factorial

import strawberryfields as sf
from strawberryfields.backends import BaseGaussianState
from sympy.utilities.iterables import multiset_permutations

[docs]def sample_to_orbit(sample: list) -> list: """Provides the orbit corresponding to a given sample. **Example usage:** >>> sample = [1, 2, 0, 0, 1, 1, 0, 3] >>> sample_to_orbit(sample) [3, 2, 1, 1, 1] Args: sample (list[int]): a sample from GBS Returns: list[int]: the orbit of the sample """ return sorted(filter(None, sample), reverse=True)
[docs]def sample_to_event(sample: list, max_count_per_mode: int) -> Union[int, None]: r"""Provides the event corresponding to a given sample. For an input max_count_per_mode, events are expressed here simply by the total photon number :math:k. **Example usage:** >>> sample = [1, 2, 0, 0, 1, 1, 0, 3] >>> sample_to_event(sample, 4) 8 >>> sample_to_event(sample, 2) None Args: sample (list[int]): a sample from GBS max_count_per_mode (int): the maximum number of photons counted in any given mode for a sample to be categorized as an event. Samples with counts exceeding this value are attributed the event None. Returns: int or None: the event of the sample """ if max(sample) <= max_count_per_mode: return sum(sample) return None
[docs]def orbit_to_sample(orbit: list, modes: int) -> list: """Generates a sample selected uniformly at random from the specified orbit. **Example usage:** >>> orbit_to_sample([2, 1, 1], 6) [0, 1, 2, 0, 1, 0] Args: orbit (list[int]): orbit to generate a sample from modes (int): number of modes in the sample Returns: list[int]: a sample in the orbit """ if modes < len(orbit): raise ValueError("Number of modes cannot be smaller than length of orbit") sample = orbit + [0] * (modes - len(orbit)) np.random.shuffle(sample) return sample
[docs]def event_to_sample(photon_number: int, max_count_per_mode: int, modes: int) -> list: """Generates a sample selected uniformly at random from the specified event. **Example usage:** >>> event_to_sample(4, 2, 6) [0, 1, 0, 0, 2, 1] Args: photon_number (int): number of photons in the event max_count_per_mode (int): maximum number of photons per mode in the event modes (int): number of modes in the sample Returns: list[int]: a sample in the event """ if max_count_per_mode < 0: raise ValueError("Maximum number of photons per mode must be non-negative") if max_count_per_mode * modes < photon_number: raise ValueError( "No valid samples can be generated. Consider increasing the " "max_count_per_mode or reducing the number of photons." ) cards = [] orbs = [] for orb in orbits(photon_number): if max(orb) <= max_count_per_mode: cards.append(orbit_cardinality(orb, modes)) orbs.append(orb) norm = sum(cards) prob = [c / norm for c in cards] orbit = orbs[np.random.choice(len(prob), p=prob)] return orbit_to_sample(orbit, modes)
[docs]def orbits(photon_number: int) -> Generator[list, None, None]: """Generate all the possible orbits for a given photon number. Provides a generator over the integer partitions of photon_number. Code derived from website <http://jeromekelleher.net/generating-integer-partitions.html>__ of Jerome Kelleher's, which is based upon an algorithm from Ref. :cite:kelleher2009generating. **Example usage:** >>> o = orbits(5) >>> list(o) [[1, 1, 1, 1, 1], [2, 1, 1, 1], [3, 1, 1], [2, 2, 1], [4, 1], [3, 2], [5]] Args: photon_number (int): number of photons to generate orbits from Returns: Generator[list[int]]: orbits with total photon number adding up to photon_number """ a = [0] * (photon_number + 1) k = 1 y = photon_number - 1 while k != 0: x = a[k - 1] + 1 k -= 1 while 2 * x <= y: a[k] = x y -= x k += 1 l = k + 1 while x <= y: a[k] = x a[l] = y yield sorted(a[: k + 2], reverse=True) x += 1 y -= 1 a[k] = x + y y = x + y - 1 yield sorted(a[: k + 1], reverse=True)
[docs]def orbit_cardinality(orbit: list, modes: int) -> int: """Gives the number of samples belonging to the input orbit. For example, there are three possible samples in the orbit [2, 1, 1] with three modes: [1, 1, 2], [1, 2, 1], and [2, 1, 1]. With four modes, there are 12 samples in total. **Example usage:** >>> orbit_cardinality([2, 1, 1], 4) 12 Args: orbit (list[int]): orbit; we count how many samples are contained in it modes (int): number of modes in the samples Returns: int: number of samples in the orbit """ sample = orbit + [0] * (modes - len(orbit)) counts = list(Counter(sample).values()) return int(factorial(modes) / np.prod(factorial(counts)))
[docs]def event_cardinality(photon_number: int, max_count_per_mode: int, modes: int) -> int: r"""Gives the number of samples belonging to the input event. For example, for three modes, there are six samples in an :math:E_{k=2, n_{\max}=2} event: [1, 1, 0], [1, 0, 1], [0, 1, 1], [2, 0, 0], [0, 2, 0], and [0, 0, 2]. **Example usage:** >>> event_cardinality(2, 2, 3) 6 Args: photon_number (int): number of photons in the event max_count_per_mode (int): maximum number of photons per mode in the event modes (int): number of modes in counted samples Returns: int: number of samples in the event """ cardinality = 0 for orb in orbits(photon_number): if max(orb) <= max_count_per_mode: cardinality += orbit_cardinality(orb, modes) return cardinality
def _get_state(graph: nx.Graph, n_mean: float = 5, loss: float = 0.0) -> BaseGaussianState: r"""Embeds the input graph into a GBS device and returns the corresponding Gaussian state.""" modes = graph.order() A = nx.to_numpy_array(graph) mean_photon_per_mode = n_mean / float(modes) p = sf.Program(modes) # pylint: disable=expression-not-assigned with p.context as q: sf.ops.GraphEmbed(A, mean_photon_per_mode=mean_photon_per_mode) | q if loss: for _q in q: sf.ops.LossChannel(1 - loss) | _q eng = sf.LocalEngine(backend="gaussian") return eng.run(p).state
[docs]def prob_orbit_exact(graph: nx.Graph, orbit: list, n_mean: float = 5, loss: float = 0.0) -> float: r"""Gives the exact probability of a given orbit for the input graph. The exact probability of an orbit is the sum of probabilities of all possible GBS output patterns that belong to it: .. math:: p(O) = \sum_{S \in O} p(S) where :math:S are samples belonging to :math:O. **Example usage:** >>> graph = nx.complete_graph(8) >>> prob_orbit_exact(graph, [2, 1, 1]) 0.03744399092424445 Args: graph (nx.Graph): input graph encoded in the GBS device orbit (list[int]): orbit for which to calculate the probability n_mean (float): total mean photon number of the GBS device loss (float): fraction of photons lost in GBS Returns: float: exact orbit probability """ if n_mean < 0: raise ValueError("Mean photon number must be non-negative") if not 0 <= loss <= 1: raise ValueError("Loss parameter must take a value between zero and one") modes = graph.order() photons = sum(orbit) state = _get_state(graph, n_mean, loss) click = orbit + [0] * (modes - len(orbit)) prob = 0 for pattern in multiset_permutations(click): prob += state.fock_prob(pattern, cutoff=photons + 1) return prob
[docs]def prob_event_exact( graph: nx.Graph, photon_number: int, max_count_per_mode: int, n_mean: float = 5, loss: float = 0.0, ) -> float: r"""Gives the exact probability of a given event for the input graph. Events are made up of multiple orbits. To calculate an event probability, we can sum over the probabilities of its constituent orbits using :func:prob_orbit_exact. **Example usage:** >>> graph = nx.complete_graph(8) >>> prob_event_exact(graph, 4, 2) 0.11077180648422322 Args: graph (nx.Graph): input graph encoded in the GBS device photon_number (int): number of photons in the event max_count_per_mode (int): maximum number of photons per mode in the event n_mean (float): total mean photon number of the GBS device loss (float): fraction of photons lost in GBS Returns: float: exact event probability """ if n_mean < 0: raise ValueError("Mean photon number must be non-negative") if not 0 <= loss <= 1: raise ValueError("Loss parameter must take a value between zero and one") if photon_number < 0: raise ValueError("Photon number must not be below zero") if max_count_per_mode < 0: raise ValueError("Maximum number of photons per mode must be non-negative") prob = 0 for orbit in orbits(photon_number): if max(orbit) <= max_count_per_mode: prob += prob_orbit_exact(graph, orbit, n_mean, loss) return prob
[docs]def prob_orbit_mc( graph: nx.Graph, orbit: list, n_mean: float = 5, samples: int = 1000, loss: float = 0.0 ) -> float: r"""Gives a Monte Carlo estimate of the probability of a given orbit for the input graph. To make this estimate, several samples from the orbit are drawn uniformly at random using :func:orbit_to_sample. The GBS probabilities of these samples are then calculated and the sum is used to create an estimate of the orbit probability. **Example usage:** >>> graph = nx.complete_graph(8) >>> prob_orbit_mc(graph, [2, 1, 1]) 0.03744399092424391 Args: graph (nx.Graph): input graph encoded in the GBS device orbit (list[int]): orbit for which to estimate the probability n_mean (float): total mean photon number of the GBS device samples (int): number of samples used in the Monte Carlo estimation loss (float): fraction of photons lost in GBS Returns: float: Monte Carlo estimated orbit probability """ if samples < 1: raise ValueError("Number of samples must be at least one") if n_mean < 0: raise ValueError("Mean photon number must be non-negative") if not 0 <= loss <= 1: raise ValueError("Loss parameter must take a value between zero and one") modes = graph.order() photons = sum(orbit) state = _get_state(graph, n_mean, loss) prob = 0 for _ in range(samples): sample = orbit_to_sample(orbit, modes) prob += state.fock_prob(sample, cutoff=photons + 1) prob *= orbit_cardinality(orbit, modes) / samples return prob
[docs]def prob_event_mc( graph: nx.Graph, photon_number: int, max_count_per_mode: int, n_mean: float = 5, samples: int = 1000, loss: float = 0.0, ) -> float: r"""Gives a Monte Carlo estimate of the probability of a given event for the input graph. To make this estimate, several samples from the event are drawn uniformly at random using :func:event_to_sample. The GBS probabilities of these samples are then calculated and the sum is used to create an estimate of the event probability. **Example usage:** >>> graph = nx.complete_graph(8) >>> prob_event_mc(graph, 4, 2) 0.11368151661229377 Args: graph (nx.Graph): input graph encoded in the GBS device photon_number (int): number of photons in the event max_count_per_mode (int): maximum number of photons per mode in the event n_mean (float): total mean photon number of the GBS device samples (int): number of samples used in the Monte Carlo estimation loss (float): fraction of photons lost in GBS Returns: float: Monte Carlo estimated event probability """ if samples < 1: raise ValueError("Number of samples must be at least one") if n_mean < 0: raise ValueError("Mean photon number must be non-negative") if not 0 <= loss <= 1: raise ValueError("Loss parameter must take a value between zero and one") if photon_number < 0: raise ValueError("Photon number must not be below zero") if max_count_per_mode < 0: raise ValueError("Maximum number of photons per mode must be non-negative") modes = graph.order() state = _get_state(graph, n_mean, loss) prob = 0 for _ in range(samples): sample = event_to_sample(photon_number, max_count_per_mode, modes) prob += state.fock_prob(sample, cutoff=photon_number + 1) prob = prob * event_cardinality(photon_number, max_count_per_mode, modes) / samples return prob
[docs]def feature_vector_orbits( graph: nx.Graph, list_of_orbits: list, n_mean: float = 5, samples: int = None, loss: float = 0.0, ) -> list: r"""Calculates feature vector of orbit probabilities for the input graph. These probabilities can be either exact or estimated using Monte Carlo methods. The argument samples is set to None to get exact feature vectors by default. To use Monte Carlo estimation, samples can be set to the number of samples desired to be used in the estimation. .. warning:: Computing exact probabilities for a large number of orbits, especially for orbits with high total photon numbers, can be quite time-consuming. For example, calculating the exact probabilities of observing 8 total photons in a 25-mode graph can take on the order of a few minutes. Monte Carlo estimation, although less precise, can be much quicker. **Example usage:** >>> graph = nx.erdos_renyi_graph(8, p=0.7, seed=0) >>> feature_vector_orbits(graph, [[1,1], [2,1,1], [1,1,1,1], [2, 2]]) [0.21962528885336693, 0.06276606666427528, 0.06542564576021911, 0.009042568926209148] >>> feature_vector_orbits(graph, [[1,1], [2,1,1], [1,1,1,1], [2, 2]], samples = 1000) [0.21559451884617312, 0.06326819519758922, 0.06381925998626868, 0.008802142975935753] Args: graph (nx.Graph): input graph list_of_orbits (list[list[int]]): a list of orbits n_mean (float): total mean photon number of the GBS device samples (int): optional number of samples used in the Monte Carlo estimation. Defaults to exact calculation if samples is unspecified. loss (float): fraction of photons lost in GBS Returns: list[float]: a feature vector of orbit probabilities in the same order as list_of_orbits """ if len(list_of_orbits) <= 0: raise ValueError("List of orbits must have at least one orbit") if any(min(elem) < 0 for elem in list_of_orbits): raise ValueError("Cannot request orbits with photon number below zero") if n_mean < 0: raise ValueError("Mean photon number must be non-negative") if not 0 <= loss <= 1: raise ValueError("Loss parameter must take a value between zero and one") if samples: return [prob_orbit_mc(graph, orbit, n_mean, samples, loss) for orbit in list_of_orbits] return [prob_orbit_exact(graph, orbit, n_mean, loss) for orbit in list_of_orbits]
[docs]def feature_vector_events( graph: nx.Graph, event_photon_numbers: list, max_count_per_mode: int = 2, n_mean: float = 5, samples: int = None, loss: float = 0.0, ) -> list: r"""Calculates feature vector of event probabilities for the input graph. These probabilities can be either exact or estimated using Monte Carlo estimation. The argument samples is set to None to get exact feature vectors by default. To use Monte Carlo estimation, samples can be set to the number of samples desired to be used in the estimation. .. warning:: Computing exact probabilities for a large number of events, especially for events with high total photon numbers, can be quite time-consuming. For example, calculating the exact probabilities of observing 8 total photons in a 25 mode graph can take on the order of a few minutes. Monte Carlo estimation, although less precise, can be much quicker. **Example usage:** >>> graph = nx.erdos_renyi_graph(8, p=0.7, seed=0) >>> feature_vector_events(graph, [2, 3, 4, 6], 1) [0.21962528885336693, 0.0, 0.06542564576021911, 0.008278357709166432] >>> feature_vector_events(graph, [2, 3, 4, 6], 1, samples = 1000) [0.21559451884617312, 0.0, 0.06445223981110329, 0.008332933548878733] Args: graph (nx.Graph): input graph event_photon_numbers (list[int]): a list of events described by their total photon number max_count_per_mode (int): maximum number of photons per mode for all events n_mean (float): total mean photon number of the GBS device samples (int): optional number of samples used in the Monte Carlo estimation. Defaults to exact calculation if samples is unspecified. loss (float): fraction of photons lost in GBS Returns: list[float]: a feature vector of event probabilities in the same order as event_photon_numbers """ if len(event_photon_numbers) <= 0: raise ValueError("List of photon numbers must have at least one element") if min(event_photon_numbers) < 0: raise ValueError("Cannot request events with photon number below zero") if max_count_per_mode < 0: raise ValueError("Maximum number of photons per mode must be non-negative") if n_mean < 0: raise ValueError("Mean photon number must be non-negative") if not 0 <= loss <= 1: raise ValueError("Loss parameter must take a value between zero and one") if samples: return [ prob_event_mc(graph, photon_number, max_count_per_mode, n_mean, samples, loss) for photon_number in event_photon_numbers ] return [ prob_event_exact(graph, photon_number, max_count_per_mode, n_mean, loss) for photon_number in event_photon_numbers ]
[docs]def feature_vector_orbits_sampling(samples: list, list_of_orbits: list) -> list: r"""Calculates feature vector of given orbits with respect to input samples. The feature vector is composed of orbit probabilities reconstructed by measuring the occurrence of given orbits in the input samples. **Example usage:** >>> from strawberryfields.apps import data >>> samples = data.Mutag0() >>> feature_vector_orbits_sampling(samples, [[1,1], [2], [2,1,1], [1,1,1,1]]) [0.19035, 0.0, 0.05175, 0.1352] Args: samples (list[list[int]]): a list of samples list_of_orbits (list[list[int]]): a list of orbits Returns: list[float]: a feature vector made up of estimated orbit probabilities in the same order as list_of_orbits """ if len(list_of_orbits) <= 0: raise ValueError("List of orbits must have at least one orbit") if any(min(elem) < 0 for elem in list_of_orbits): raise ValueError("Cannot request orbits with photon number below zero") n_samples = len(samples) sample_orbits = [sample_to_orbit(each) for each in samples] count_orbits = Counter(map(tuple, sample_orbits)) return [count_orbits[tuple(orb)] / n_samples for orb in list_of_orbits]
[docs]def feature_vector_events_sampling( samples: list, event_photon_numbers: list, max_count_per_mode: int = 2 ) -> list: r"""Calculates feature vector of given events with respect to input samples. The feature vector is composed of event probabilities reconstructed by measuring the occurrence of given events in the input samples. **Example usage:** >>> from strawberryfields.apps import data >>> samples = data.Mutag0() >>> feature_vector_events_sampling(samples, [2, 4, 6], 2) [0.19035, 0.2047, 0.1539] Args: samples (list[list[int]]): a list of samples event_photon_numbers (list[int]): a list of events described by their total photon number max_count_per_mode (int): maximum number of photons per mode for all events Returns: list[float]: a feature vector made up of estimated event probabilities in the same order as event_photon_numbers """ if len(event_photon_numbers) <= 0: raise ValueError("List of photon numbers must have at least one element") if min(event_photon_numbers) < 0: raise ValueError("Cannot request events with photon number below zero") if max_count_per_mode < 0: raise ValueError("Maximum number of photons per mode must be non-negative") n_samples = len(samples) e = (sample_to_event(s, max_count_per_mode) for s in samples) count = Counter(e) return [count[p] / n_samples for p in event_photon_numbers]