Photonic Quantum-Accelerated Machine Learning

TL;DR

This paper demonstrates how boson sampling, a quantum interference protocol, can be used to accelerate classical machine learning tasks, showing improved performance and scalability on actual photonic quantum hardware.

Contribution

It introduces a quantum accelerator leveraging boson sampling for reservoir computing, with experimental validation on a photonic quantum processing unit.

Findings

Robust performance improvements with imperfect photon sources

Effective classification with severe class imbalances

Maintains accuracy with twenty times less training data

Abstract

Machine learning is widely applied in modern society, but has yet to capitalise on the unique benefits offered by quantum resources. Boson sampling -- a quantum-interference based sampling protocol -- is a resource that is classically hard to simulate and can be implemented on current quantum hardware. Here, we present a quantum accelerator for classical machine learning, using boson sampling to provide a high-dimensional quantum fingerprint for reservoir computing. We show robust performance improvements under various conditions: imperfect photon sources down to complete distinguishability; scenarios with severe class imbalances, classifying both handwritten digits and biomedical images; and sparse data, maintaining model accuracy with twenty times less training data. Crucially, we demonstrate the acceleration and scalability of our scheme on a photonic quantum processing unit, providing the first experimental validation that boson-sampling-enhanced learning delivers real performance gains on actual quantum hardware.