Experimental quantum-enhanced kernel-based machine learning on a photonic processor
- Author(s)
- 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
- 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.
- Organisation(s)
- Research Network Quantum Aspects of Space Time, Quantum Optics, Quantum Nanophysics and Quantum Information
- External organisation(s)
- Quantinuum, Politecnico di Milano, Institute of Genetics and Biophysics "Adriano Buzzati-Traverso", CNR, Österreichische Akademie der Wissenschaften (ÖAW)
- Journal
- Nature Photonics
- No. of pages
- 9
- ISSN
- 1749-4885
- DOI
- https://doi.org/10.1038/s41566-025-01682-5
- Publication date
- 06-2025
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 103025 Quantum mechanics, 103026 Quantum optics
- ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials, Atomic and Molecular Physics, and Optics
- Portal url
- https://ucrisportal.univie.ac.at/en/publications/101d6541-c0eb-4ae5-977e-bf10e7e9f8d2