Optimal Haar random fermionic linear optics circuits

TL;DR

This paper develops optimal algorithms for sampling Haar-random fermionic linear optics circuits, achieving minimal depth and gate count, which enhances quantum simulation and benchmarking capabilities.

Contribution

It introduces efficient algorithms for generating Haar-random fermionic linear optics circuits with optimal depth and gate complexity, improving over previous methods.

Findings

Achieves circuit depth of Θ(n) with Θ(n^2) gates for active and passive FLO

Provides classical algorithms with Θ(n^2) overhead for sampling

Constructs quantum circuits with Θ(n^2) gates for Clifford FLO sampling

Abstract

Sampling unitary Fermionic Linear Optics (FLO), or matchgate circuits, has become a fundamental tool in quantum information. Such capability enables a large number of applications ranging from randomized benchmarking of continuous gate sets, to fermionic classical shadows. In this work, we introduce optimal algorithms to sample over the non-particle-preserving (active) and particle-preserving (passive) FLO Haar measures. In particular, we provide appropriate distributions for the gates of $n$-qubit parametrized circuits which produce random active and passive FLO. In contrast to previous approaches, which either incur classical $\mathcal{O}(n^3)$ compilation costs or have suboptimal depths, our methods directly output circuits which simultaneously achieve an optimal down-to-the-constant-factor $\Theta(n)$ depth and $\Theta(n^2)$ gate count; with only a $\Theta(n^2)$ classical overhead. Finally, we also provide quantum circuits to sample Clifford FLO with an optimal $\Theta(n^2)$ gate count.