TY - JOUR
T1 - Perceval
T2 - A Software Platform for Discrete Variable Photonic Quantum Computing
AU - Heurtel, Nicolas
AU - Fyrillas, Andreas
AU - de Gliniasty, Grégoire
AU - Le Bihan, Raphaël
AU - Malherbe, Sébastien
AU - Pailhas, Marceau
AU - Bertasi, Eric
AU - Bourdoncle, Boris
AU - Emeriau, Pierre Emmanuel
AU - Mezher, Rawad
AU - Music, Luka
AU - Belabas, Nadia
AU - Valiron, Benoît
AU - Senellart, Pascale
AU - Mansfield, Shane
AU - Senellart, Jean
N1 - Publisher Copyright:
© 2022 Institute of Biotechnology and Genetic Engineering. All rights reserved.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - 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.
AB - 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.
U2 - 10.22331/Q-2023-02-21-931
DO - 10.22331/Q-2023-02-21-931
M3 - Article
AN - SCOPUS:85153084984
SN - 2521-327X
VL - 7
JO - Quantum
JF - Quantum
M1 - 931
ER -