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arXiv · 2512.08318

Photonic Quantum-Accelerated Machine Learning

Markus Rambach·Abhishek Roy·Alexei Gilchrist·Akitada Sakurai·William J. Munro·Kae Nemoto·Andrew G. White
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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 of our scheme on a photonic quantum processing unit, providing experimental validation that boson-sampling-enhanced learning with Fock states delivers real performance gains on actual quantum hardware.

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Markus Rambach, Abhishek Roy, Alexei Gilchrist, Akitada Sakurai, William J. Munro, Kae Nemoto, Andrew G. White. 2025-12-09. Photonic Quantum-Accelerated Machine Learning. https://arxiv.org/abs/2512.08318

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