Photonic counterdiabatic quantum optimization algorithm

TL;DR

This paper introduces a photonic counterdiabatic quantum optimization algorithm that reduces quantum operations, enabling practical solutions for complex continuous-variable problems on near-term quantum hardware.

Contribution

It presents a novel hybrid quantum-classical algorithm inspired by counterdiabatic protocols, optimized for photonic quantum computing, with demonstrated experimental feasibility.

Findings

Outperforms existing hybrid adiabatic algorithms in convergence

Enables tackling non-convex and infinite integer optimization problems

Successfully demonstrated on an eight-mode nanophotonic chip

Abstract

We propose a hybrid quantum-classical approximate optimization algorithm for photonic quantum computing, specifically tailored for addressing continuous-variable optimization problems. Inspired by counterdiabatic protocols, our algorithm significantly reduces the required quantum operations for optimization as compared to adiabatic protocols. This reduction enables us to tackle non-convex continuous optimization and countably infinite integer programming within the near-term era of quantum computing. Through comprehensive benchmarking, we demonstrate that our approach outperforms existing state-of-the-art hybrid adiabatic quantum algorithms in terms of convergence and implementability. Remarkably, our algorithm offers a practical and accessible experimental realization, bypassing the need for high-order operations and overcoming experimental constraints. We conduct proof-of-principle experiments on an eight-mode nanophotonic quantum chip, successfully showcasing the feasibility and potential impact of the algorithm.