Experimental investigation of single qubit quantum classifier with small number of samples

TL;DR

This study demonstrates that a silicon photonic quantum classifier can achieve high accuracy with very few photon samples, highlighting its potential for resource-efficient quantum machine learning.

Contribution

First experimental demonstration of a single-qubit photonic quantum classifier operating effectively with minimal photon samples.

Findings

Achieved nearly 90% accuracy with about two photons per input.

Performance improves with larger training datasets at low sample sizes.

Experimental results align with numerical simulations.

Abstract

We experimentally investigated a single-qubit quantum classifier implemented on a silicon photonic integrated circuit, focusing on its performance under photon-limited conditions. Using the Data Reuploading method with layer-wise optimization via Sequential Minimal Optimization (SMO), input data were encoded into the photonic circuit, and classification was performed based on output detection probabilities. Heralded single photons, generated via spontaneous four-wave mixing in a silicon waveguide, served as the input states. Even when the average number of photon samples per input was reduced to approximately two, the classifier achieved nearly 90\% accuracy, provided that the training dataset was sufficiently large. The experimental results were consistent with numerical simulations, which also indicated that performance at low sample sizes can be improved by increasing the size of the training dataset. These findings demonstrate that photonic quantum classifiers can operate effectively with very few photons, supporting their practical feasibility for resource-efficient quantum machine learning on integrated photonic platforms.