Continuous-variable quantum kernel method on a programmable photonic quantum processor

TL;DR

This paper demonstrates the implementation of a continuous-variable quantum kernel method on a programmable photonic quantum processor, showing its effectiveness for classification tasks and robustness against imperfections, highlighting the potential of CV quantum systems in machine learning.

Contribution

It is the first experimental demonstration of a CV quantum kernel method on a photonic processor, expanding quantum machine learning capabilities beyond qubit systems.

Findings

Successfully classified datasets with high accuracy

Robust performance under experimental imperfections

Comparable to classical kernel methods

Abstract

Among various quantum machine learning (QML) algorithms, the quantum kernel method has especially attracted attention due to its compatibility with noisy intermediate-scale quantum devices and its potential to achieve quantum advantage. This method performs classification and regression by nonlinearly mapping data into quantum states in a higher dimensional Hilbert space. Thus far, the quantum kernel method has been implemented only on qubit-based systems, but continuous-variable (CV) systems can potentially offer superior computational power by utilizing its infinite-dimensional Hilbert space. Here, we demonstrate the implementation of the classification task with the CV quantum kernel method on a programmable photonic quantum processor. We experimentally prove that the CV quantum kernel method successfully classifies several datasets robustly even under the experimental imperfections, with high accuracies comparable to the classical kernel. This demonstration sheds light on the utility of CV quantum systems for QML and should stimulate further study in other CV QML algorithms.