Solving graph problems with single-photons and linear optics

TL;DR

This paper introduces a method to encode graph-related matrices into linear optical circuits for quantum processing, enabling the solution of various graph problems using photonic quantum devices with experimental validation.

Contribution

It presents a novel encoding technique for matrices into linear optical circuits and demonstrates their application to solve multiple graph problems on photonic quantum processors.

Findings

Successfully encoded matrices into optical circuits for graph problems

Demonstrated solutions for perfect matchings, graph isomorphism, and densest subgraph

Validated methods with numerical simulations and experimental implementation

Abstract

An important challenge for current and near-term quantum devices is finding useful tasks that can be preformed on them. We first show how to efficiently encode a bounded $n \times n$ matrix $A$ into a linear optical circuit with $2n$ modes. We then apply this encoding to the case where $A$ is a matrix containing information about a graph $G$. We show that a photonic quantum processor consisting of single-photon sources, a linear optical circuit encoding $A$, and single-photon detectors can solve a range of graph problems including finding the number of perfect matchings of bipartite graphs, computing permanental polynomials, determining whether two graphs are isomorphic, and the $k$-densest subgraph problem. We also propose pre-processing methods to boost the probabilities of observing the relevant detection events and thus improve performance. Finally we present both numerical simulations and implementations on Quandela's Ascella photonic quantum processor to validate our findings.