Photonic Quantum-Accelerated Machine Learning

• URL: http://arxiv.org/abs/2512.08318v1
• Date: Tue, 09 Dec 2025 07:32:45 GMT
• Title: Photonic Quantum-Accelerated Machine Learning
• Authors: Markus Rambach, Abhishek Roy, Alexei Gilchrist, Akitada Sakurai, William J. Munro, Kae Nemoto, Andrew G. White,
• Abstract summary: We present a quantum accelerator for classical machine learning.<n>We use boson sampling to provide a high-dimensional quantum fingerprint for reservoir computing.<n>We show robust performance improvements under various conditions.
• Score: 0.6654914040895585
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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