Experimental quantum-enhanced kernel-based machine learning on a photonic processor

Recently, machine learning has had remarkable impact in scientific to everyday-life applications. However, complex tasks often require the consumption of unfeasible amounts of energy and computational power. Quantum computation may lower such requirements, although it is unclear whether enhancements are reachable with current technologies. Here we demonstrate a kernel method on a photonic integrated processor to perform a binary classification task. We show that our protocol outperforms state-of-the-art kernel methods such as gaussian and neural tangent kernels by exploiting quantum interference, and provides further improvements in accuracy by offering single-photon coherence. Our scheme does not require entangling gates and can modify the system dimension through additional modes and injected photons. This result gives access to more efficient algorithms and to formulating tasks where quantum effects improve standard methods.

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Publication Details

Journal
Nature Photonics
Published
2025-06-02
DOI
https://doi.org/10.1038/s41566-025-01682-5
Citations
14
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
34.73

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article

Experimental quantum-enhanced kernel-based machine learning on a photonic processor

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14 citations
Nature Photonics
Neural Networks and Reservoir Computing
34.73
article

Experimental quantum-enhanced kernel-based machine learning on a photonic processor

Francesco Ceccarelli, Bob Coecke, Andrea Crespi, Philip Walther, Zhenghao Yin, Roberto Osellame, Iris Agresti, Simone Piacentini, Ciro Pentangelo, Alexis Toumi, Giovanni de Felice, Douglas Brown
article en
14 citations

Abstract

Recently, machine learning has had remarkable impact in scientific to everyday-life applications. However, complex tasks often require the consumption of unfeasible amounts of energy and computational power. Quantum computation may lower such requirements, although it is unclear whether enhancements are reachable with current technologies. Here we demonstrate a kernel method on a photonic integrated processor to perform a binary classification task. We show that our protocol outperforms state-of-the-art kernel methods such as gaussian and neural tangent kernels by exploiting quantum interference, and provides further improvements in accuracy by offering single-photon coherence. Our scheme does not require entangling gates and can modify the system dimension through additional modes and injected photons. This result gives access to more efficient algorithms and to formulating tasks where quantum effects improve standard methods.

Nature PhotonicsVol. 19(9)
University of Vienna (AT), Austrian Academy of Sciences (AT), Institute for Quantum Optics and Quantum Information Innsbruck (AT), Vienna Center for Quantum Science and Technology (AT), Christian Doppler Laboratory for Thermoelectricity (AT), Istituto di Fotonica e Nanotecnologie (IT), Politecnico di Milano (IT)
National Research Centre, Österreichische Nationalstiftung für Forschung, Technologie und Entwicklung, European Commission, Austrian Science Fund, Universität Wien, Christian Doppler Forschungsgesellschaft, Politecnico di Milano, QuantERA
Openalex Percentile: Top 0%
Neural Networks and Reservoir Computing
34.73
DOIOpen Access PDFOpenAlex
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