Perceval: A Software Platform for Discrete Variable Photonic Quantum Computing

TL;DR

Perceval is an open-source software platform that enables simulation, design, and optimization of discrete-variable photonic quantum computing experiments and algorithms, supporting both weak and strong simulation techniques.

Contribution

It introduces a comprehensive, flexible Python-based toolkit for modeling and simulating photonic quantum circuits with multiple optimized back-ends.

Findings

Successfully reproduces various photonic experiments

Simulates quantum algorithms like Grover's and Shor's

Supports applications in quantum machine learning

Abstract

We introduce Perceval, an open-source software platform for simulating and interfacing with discrete-variable photonic quantum computers, and describe its main features and components. Its Python front-end allows photonic circuits to be composed from basic photonic building blocks like photon sources, beam splitters, phase-shifters and detectors. A variety of computational back-ends are available and optimised for different use-cases. These use state-of-the-art simulation techniques covering both weak simulation, or sampling, and strong simulation. We give examples of Perceval in action by reproducing a variety of photonic experiments and simulating photonic implementations of a range of quantum algorithms, from Grover's and Shor's to examples of quantum machine learning. Perceval is intended to be a useful toolkit for experimentalists wishing to easily model, design, simulate, or optimise a discrete-variable photonic experiment, for theoreticians wishing to design algorithms and applications for discrete-variable photonic quantum computing platforms, and for application designers wishing to evaluate algorithms on available state-of-the-art photonic quantum computers.