Experimental quantum-enhanced kernels on a photonic processor

TL;DR

This paper demonstrates a quantum-enhanced kernel method on a photonic processor that outperforms classical kernels in binary classification, leveraging quantum interference without entangling gates, and suggests potential for more efficient quantum algorithms.

Contribution

It introduces a quantum kernel method on a photonic processor that surpasses classical kernels, using quantum interference and scalable system modifications without entangling gates.

Findings

Outperforms classical kernel methods like Gaussian and neural tangent kernels.

Uses quantum interference to enhance classification performance.

Does not require entangling gates, enabling scalable system modifications.

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.