Approximating outcome probabilities of linear optical circuits

TL;DR

This paper introduces classical algorithms for efficiently approximating outcome probabilities in linear optical circuits by leveraging quasiprobability distributions, reducing negativity bounds, and enabling polynomial-time estimation in high classicality regimes.

Contribution

It presents novel quantum-inspired algorithms that improve outcome probability estimation and provide conditions for classical simulability of Gaussian boson sampling.

Findings

Negativity bounds can be reduced from exponential to polynomial in specific cases.

Efficient polynomial-time estimation algorithms are possible when the circuit's classicality is high.

Provides conditions under which Gaussian boson sampling is classically simulable.

Abstract

Quasiprobability representation is an important tool for analyzing a quantum system, such as a quantum state or a quantum circuit. In this work, we propose classical algorithms specialized for approximating outcome probabilities of a linear optical circuit using $s$-parameterized quasiprobability distributions. Notably, we can reduce the negativity bound of a circuit from exponential to at most polynomial for specific cases by modulating the shapes of quasiprobability distributions thanks to the norm-preserving property of a linear optical transformation. Consequently, our scheme renders an efficient estimation of outcome probabilities with precision depending on the classicality of the circuit. Surprisingly, when the classicality is high enough, we reach a polynomial-time estimation algorithm within a multiplicative error. Our results provide quantum-inspired algorithms for approximating various matrix functions beating best-known results. Moreover, we give sufficient conditions for the classical simulability of Gaussian boson sampling using the approximating algorithm for any (marginal) outcome probability under the poly-sparse condition. Our study sheds light on the power of linear optics, providing plenty of quantum-inspired algorithms for problems in computational complexity.