What can quantum optics say about computational complexity theory?

TL;DR

This paper explores the computational complexity of sampling from output distributions of linear-optical networks with Gaussian states, showing that certain probabilities relate to matrix permanents and can be approximated efficiently classically.

Contribution

It derives a general formula for output probabilities, links them to positive-semidefinite matrix permanents, and demonstrates an efficient classical sampling algorithm for specific cases.

Findings

Output probabilities proportional to permanents of positive-semidefinite matrices

Existence of an efficient classical sampling algorithm for certain Gaussian states

Complexity analysis of sampling with squeezed-vacuum states

Abstract

Considering the problem of sampling from the output photon-counting probability distribution of a linear-optical network for input Gaussian states, we obtain results that are of interest from both quantum theory and the computational complexity theory point of view. We derive a general formula for calculating the output probabilities, and by considering input thermal states, we show that the output probabilities are proportional to permanents of positive-semidefinite Hermitian matrices. It is believed that approximating permanents of complex matrices in general is a #P-hard problem. However, we show that these permanents can be approximated with an algorithm in BPP^NP complexity class, as there exists an efficient classical algorithm for sampling from the output probability distribution. We further consider input squeezed-vacuum states and discuss the complexity of sampling from the probability distribution at the output.