Salvato in:
| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2512.08318 |
| Tags: |
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Sommario:
- 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.