Proof of Hiding Conjecture in Gaussian Boson Sampling

TL;DR

This paper proves the hiding conjecture for Gaussian boson sampling with maximal squeezed states, establishing that certain submatrices can be approximated by Gaussian matrices, supporting the classical hardness of simulating GBS.

Contribution

It provides the first rigorous proof of the hiding conjecture in the experimentally relevant regime of GBS with maximal squeezed states.

Findings

Proves the hiding conjecture for GBS with maximal squeezed states.

Shows that submatrices of COE matrices can be approximated by Gaussian matrices.

Supports the classical hardness of simulating GBS in experimental setups.

Abstract

Gaussian boson sampling (GBS) is a promising protocol for demonstrating quantum computational advantage. One of the key steps for proving classical hardness of GBS is the so-called ``hiding conjecture'', which asserts that one can ``hide'' a complex Gaussian matrix as a submatrix of the outer product of Haar unitary submatrices in total variation distance. In this paper, we prove the hiding conjecture for input states with the maximal number of squeezed states, which is a setup that has recently been realized experimentally [Madsen et al., Nature 606, 75 (2022)]. In this setting, the hiding conjecture states that a $o(\sqrt{M})\times o(\sqrt{M})$ submatrix of an $M\times M$ circular orthogonal ensemble (COE) random matrix can be well-approximated by a complex Gaussian matrix in total variation distance as $M\to\infty$. This is the first rigorous proof of the hiding property for GBS in the experimentally relevant regime, and puts the argument for hardness of classically simulating GBS with a maximal number of squeezed states on a comparable level to that of the conventional boson sampling of [Aaronson and Arkhipov, Theory Comput. 9, 143 (2013)].