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Quantum optical shallow networks

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arxiv 2507.21036 v3 pith:5JS5F6A4 submitted 2025-07-28 quant-ph

Quantum optical shallow networks

classification quant-ph
keywords neuronsnumberinputopticalshallowarbitrarynetworknetworks
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Divide et impera: hybrid multinomial classifiers from quantum binary models

    quant-ph 2026-04 unverdicted novelty 6.0

    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.

  2. Quantum optical neural networks using atom-cavity interactions to provide all-optical nonlinearity

    quant-ph 2025-11 conditional novelty 6.0

    A simulated neural network uses atom-cavity two-level neurons as all-optical nonlinear activations and reports ~95% accuracy on MNIST and SAT-6.

  3. Quantum Optical Reinforcement Learning via Spectrum-Resolved Hong-Ou-Mandel Interference

    quant-ph 2026-07 conditional novelty 5.0

    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.