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

Autor(en)

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

Abstrakt

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.

Organisation(en)

Forschungsverbund Quantum Aspects of Space Time, Quantenoptik, Quantennanophysik und Quanteninformation

Externe Organisation(en)

Quantinuum, Politecnico di Milano, Consiglio Nazionale delle Ricerche, Österreichische Akademie der Wissenschaften (ÖAW)

Journal

Nature Photonics

Anzahl der Seiten

9

ISSN

1749-4885

DOI

https://doi.org/10.1038/s41566-025-01682-5

Publikationsdatum

06-2025

Peer-reviewed

Ja

ÖFOS 2012

103025 Quantenmechanik, 103026 Quantenoptik

ASJC Scopus Sachgebiete

Electronic, Optical and Magnetic Materials, Atomic and Molecular Physics, and Optics

Link zum Portal

https://ucrisportal.univie.ac.at/de/publications/101d6541-c0eb-4ae5-977e-bf10e7e9f8d2