# sf.apps.similarity.feature_vector_orbits¶

feature_vector_orbits(graph, list_of_orbits, n_mean=5, samples=None, loss=0.0)[source]

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]

Parameters
• 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

a feature vector of orbit probabilities in the same order as list_of_orbits

Return type

list[float]