Demonstration of hardware efficient photonic variational quantum algorithm

Author(s)
Iris Agresti, Koushik Paul, Peter Schiansky, Simon Steiner, Zhenghao Yin, Ciro Pentangelo, Simone Piacentini, Andrea Crespi, Yue Ban, Francesco Ceccarelli, Roberto Osellame, Xi Chen, Philip Walther
Abstract

Quantum computing has changed the paradigm of computer science, where quantum technologies have promised to outperform classical ones. Such an advantage was only demonstrated for tasks with no application or out of reach for the state-of-the-art quantum technologies. In this context, a promising strategy to find practical use of quantum computers exploits hybrid models, where a quantum device estimates a hard-to-compute quantity, while a classical optimizer trains the parameters. In this work, we demonstrate that single photons and linear optical networks are sufficient for implementing variational quantum algorithms, when the problem specification (ansatz) is tailored to this specific platform. We show this by a proof-of-principle demonstration to tackle an instance of a factorization task, whose solution is encoded in the ground state of a suitable Hamiltonian. This work, which combines Variational Quantum Algorithms with hardware efficient ansätze for linear optics, showcases a promising pathway toward practical applications for photonic quantum platforms.

Organisation(s)
Quantum Optics, Quantum Nanophysics and Quantum Information, Electronic Properties of Materials
External organisation(s)
University of the Basque Country, QUBO Technology GmbH, Institute of Genetics and Biophysics "Adriano Buzzati-Traverso", CNR, Politecnico di Milano, Spanish National Research Council (CSIC), Österreichische Akademie der Wissenschaften (ÖAW)
Journal
Physical Review Research
Volume
7
No. of pages
8
ISSN
2643-1564
DOI
https://doi.org/10.48550/arXiv.2408.10339
Publication date
10-2025
Peer reviewed
Yes
Austrian Fields of Science 2012
102040 Quantum computing, 103026 Quantum optics
ASJC Scopus subject areas
General Physics and Astronomy
Portal url
https://ucrisportal.univie.ac.at/en/publications/967dbb5a-a035-4f70-b0e6-eea53d32b8d4