Experimental quantum-enhanced kernels on a photonic processor
Abstract
Recently, machine learning had a remarkable impact, from scientific to everyday-life applications. However, complex tasks often imply unfeasible energy and computational power consumption. Quantum computation might lower such requirements, although it is unclear whether enhancements are reachable by current technologies. Here, we demonstrate a kernel method on a photonic integrated processor to perform a binary classification. We show that our protocol outperforms state-of-the-art kernel methods including gaussian and neural tangent kernels, exploiting quantum interference, and brings a smaller improvement also by single photon coherence. Our scheme does not require entangling gates and can modify the system dimension through additional modes and injected photons. This result opens to more efficient algorithms and to formulating tasks where quantum effects improve standard methods.
Cite
@article{arxiv.2407.20364,
title = {Experimental quantum-enhanced kernels on a photonic processor},
author = {Zhenghao Yin and Iris Agresti and Giovanni de Felice and Douglas Brown and Alexis Toumi and Ciro Pentangelo and Simone Piacentini and Andrea Crespi and Francesco Ceccarelli and Roberto Osellame and Bob Coecke and Philip Walther},
journal= {arXiv preprint arXiv:2407.20364},
year = {2024}
}