Applications of Near-Term Photonic Quantum Computers: Software and Algorithms

TL;DR

This paper introduces a new software layer for photonic quantum computers based on Gaussian Boson Sampling, enabling easier implementation of algorithms for graph problems, chemistry, and more, while reviewing current GBS algorithms.

Contribution

It presents a novel applications layer for the Strawberry Fields library, simplifying the programming of GBS algorithms and serving as a review of the state of the art.

Findings

Software enables quick implementation of GBS algorithms

Supports applications in graph problems, chemistry, and point processes

Serves as both software introduction and state-of-the-art review

Abstract

Gaussian Boson Sampling (GBS) is a near-term platform for photonic quantum computing. Recent efforts have led to the discovery of GBS algorithms with applications to graph-based problems, point processes, and molecular vibronic spectra in chemistry. The development of dedicated quantum software is a key enabler in permitting users to program devices and implement algorithms. In this work, we introduce a new applications layer for the Strawberry Fields photonic quantum computing library. The applications layer provides users with the necessary tools to design and implement algorithms using GBS with only a few lines of code. This paper serves a dual role as an introduction to the software, supported with example code, and also a review of the current state of the art in GBS algorithms.