Demonstration of hardware efficient photonic variational quantum algorithm

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.

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Publication Details

Journal
Physical Review Research
Published
2025-10-08
DOI
https://doi.org/10.1103/d7bb-ybfh
Citations
2
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
4.96
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Demonstration of hardware efficient photonic variational quantum algorithm

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Demonstration of hardware efficient photonic variational quantum algorithm

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article en
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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.

Physical Review ResearchVol. 7(4)
University of Vienna (AT), AIT Austrian Institute of Technology GmbH (AT), Austrian Academy of Sciences (AT), University of the Basque Country (ES), Institute for Quantum Optics and Quantum Information Innsbruck (AT), Vienna Center for Quantum Science and Technology (AT), Instituto de Ciencia de Materiales de Madrid (ES), Istituto di Fotonica e Nanotecnologie (IT), Politecnico di Milano (IT)
Openalex Percentile: Top 5%
Neural Networks and Reservoir Computing
4.96
DOIOpen Access PDFOpenAlex
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