Large-scale quantum reservoir computing using a Gaussian Boson Sampler

TL;DR

This paper demonstrates that a large-scale Gaussian Boson Sampler can serve as an effective quantum reservoir computer, leveraging quantum correlations to improve machine learning accuracy on benchmark tasks.

Contribution

It provides the first experimental validation that a GBS can be used as a quantum reservoir computer and explores the role of quantum correlations in enhancing performance.

Findings

Correlations in GBS improve classification accuracy by over 20 percentage points.

Squeezed light sources yield higher accuracies than classical sources.

The GBS-based reservoir computer performs well on spoken-vowels and MNIST digit classification.

Abstract

A Gaussian boson sampler (GBS) is a special-purpose quantum computer that can be practically realized at large scale in optics. Here we report on experiments in which we used a frequency-multiplexed GBS with $>400$ modes as the reservoir in the quantum-machine-learning approach of quantum reservoir computing. We evaluated the accuracy of our GBS-based reservoir computer on a variety of benchmark tasks, including spoken-vowels classification and MNIST handwritten-digit classification. We found that when the reservoir computer was given access to the correlations between measured modes of the GBS, the achieved accuracies were the same or higher than when it was only given access to the mean photon number in each mode -- and in several cases the advantage in accuracy from using the correlations was greater than 20 percentage points. This provides experimental evidence in support of theoretical predictions that access to correlations enhances the power of quantum reservoir computers. We also tested our reservoir computer when operating the reservoir with various sources of classical rather than squeezed (quantum) light and found that using squeezed light consistently resulted in the highest (or tied highest, for simple tasks) accuracies. Our work experimentally establishes that a GBS can be an effective reservoir for quantum reservoir computing and provides a practical platform for experimentally exploring the role of quantumness and correlations in quantum machine learning at very large system sizes.