Quantum-Accelerated Machine Learning

Speaker: Prof Andrew White
Affiliation: University of Queensland

Abstract

Science and technology are intertwined, with advances in one opening up new vistas for both. In this talk we will look at why quantum computing is interesting to scientists—as opposed to bankers and spies—and how exploring that question led to us exploring whether machine learning can be accelerated by quantum phenomena. Machine learning is widely applied in modern society, but has yet to capitalise on the unique benefits offered by quantum resources. It turns out that photons scattering through a network of beamsplitters is a powerful quantum resource: it is *very* hard to simulate, either with classical or quantum computers. Even better, it can be implemented on current quantum hardware. We explore accelerating quantum machine learning, using quantum optics to provide a high-dimensional quantum fingerprint for reservoir computing [1,2]. We find robust performance improvements under various conditions: imperfect photon sources—even down to complete photon 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. Our results demonstrate that quantum photonic processors can accelerate practical ML workloads today [2]. Scaling photonic platforms, improved sampling, and applying QORC to temporal tasks represent natural directions for extending quantum reservoir computing.

[1].        A. Sakurai, A. Hayashi, W. J. Munro and K. Nemoto, Optica Quantum 3, 238 (2025)

[2].        M. Rambach, A. Roy, A. Gilchrist, A. Sakurai, W. J. Munro, K. Nemoto and A. G. White, arXiv 2512.08318 (2025)