Quantum Computational Advantage via High-Dimensional Gaussian Boson Sampling

TL;DR

This paper advances quantum computational advantage using high-dimensional Gaussian boson sampling, offering a more programmable and low-loss approach that outperforms classical simulations at modest system sizes.

Contribution

It provides rigorous evidence for GBS hardness and introduces a new high-dimensional GBS architecture that is programmable and experimentally feasible.

Findings

High-dimensional GBS outperforms classical simulations at modest sizes.

The proposed architecture is low-loss and programmable.

Provides evidence for the classical hardness of GBS.

Abstract

Photonics is a promising platform for demonstrating a quantum computational advantage (QCA) by outperforming the most powerful classical supercomputers on a well-defined computational task. Despite this promise, existing proposals and demonstrations face challenges. Experimentally, current implementations of Gaussian boson sampling (GBS) lack programmability or have prohibitive loss rates. Theoretically, there is a comparative lack of rigorous evidence for the classical hardness of GBS. In this work, we make progress in improving both the theoretical evidence and experimental prospects. We provide evidence for the hardness of GBS, comparable to the strongest theoretical proposals for QCA. We also propose a new QCA architecture we call high-dimensional GBS, which is programmable and can be implemented with low loss using few optical components. We show that particular algorithms for simulating GBS are outperformed by high-dimensional GBS experiments at modest system sizes. This work thus opens the path to demonstrating QCA with programmable photonic processors.