Extension of a Pattern Recognition Validation Approach for Noisy Boson Sampling

TL;DR

This paper extends a pattern recognition validation method to evaluate noisy boson sampling, considering photon distinguishability and loss, and analyzes how these noises affect the output data structure and validation performance.

Contribution

It introduces an extended validation approach for boson sampling that accounts for both distinguishability and loss, enhancing the assessment of quantum advantage under realistic noise conditions.

Findings

Distribution of characteristic values changes monotonically with indistinguishability

Photon loss suppresses the regulation of characteristic value distribution

Data structure analysis reveals how noise impacts output event distributions

Abstract

Boson sampling is one of the main quantum computation models to demonstrate the quantum computational advantage. However, this aim may be hard to realize considering two main kinds of noises, which are photon distinguishability and photon loss. Inspired by the Bayesian validation extended to evaluate whether distinguishability is too high to demonstrate this advantage, the pattern recognition validation is extended for boson sampling, considering both distinguishability and loss. Based on clusters constructed with the K means++ method, where parameters are carefully adjusted to optimize the extended validation performances, the distribution of characteristic values is nearly monotonically changed with indistinguishability, especially when photons are close to be indistinguishable. However, this regulation may be suppressed by photon loss. The intrinsic data structure of output events is analyzed through calculating probability distributions and mean 2-norm distances of the sorted outputs. An approximation algorithm is also used to show the data structure changes with noises.