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Quantum optical shallow networks
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Quantum optical shallow networks
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Classical shallow networks are universal approximators. Given a sufficient number of neurons, they can reproduce any continuous function to arbitrary precision, with a resource cost that scales linearly in both the input size and the number of trainable parameters. In this work, we present a quantum optical protocol that implements a shallow network with an arbitrary number of neurons. Both the input data and the parameters are encoded into single-photon states. Leveraging the Hong-Ou-Mandel effect, the network output is determined by the coincidence rates measured when the photons interfere at a beam splitter, with multiple neurons prepared as a mixture of single-photon states. Remarkably, once trained, our model requires constant optical resources regardless of the number of input features and neurons.
Forward citations
Cited by 3 Pith papers
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Binary decision trees enable cost-effective multinomial classifiers from quantum binary models, matching other methods' accuracy with at most logarithmic overhead in the number of classes.
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A simulated neural network uses atom-cavity two-level neurons as all-optical nonlinear activations and reports ~95% accuracy on MNIST and SAT-6.
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Quantum Optical Reinforcement Learning via Spectrum-Resolved Hong-Ou-Mandel Interference
A spectrum-resolved HOM interference readout, mapped into an actor-critic, is claimed to outperform matching MLP agents on continuous-control benchmarks and to restore drifted transmon-gate fidelities in simulation.
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