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

• URL: http://arxiv.org/abs/2607.26438v1
• Date: Wed, 29 Jul 2026 03:37:01 GMT
• Title: Quantum Optical Reinforcement Learning via Spectrum-Resolved Hong-Ou-Mandel Interference
• Abstract summary: Hong-Ou-Mandel interference-based optical neural networks can offer complexity advantages on benchmark learning tasks.<n>We introduce a spectrum-resolved HOM architecture that promotes the photons' spectral degrees of freedom to a trainable computational resource.
• Score: 9.506782998504764
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Hong-Ou-Mandel (HOM) interference-based optical neural networks can offer complexity advantages on benchmark learning tasks, but conventional readout compresses the coincidence spectrum into a single scalar, limiting its use in complex settings such as continuous-action reinforcement learning. Here we introduce a spectrum-resolved HOM (SR-HOM) architecture that promotes the photons' spectral degrees of freedom to a trainable computational resource and use it to construct a compact optical actor-critic agent. Diagonal spectral responses generate continuous actions, while higher-order spectral correlations provide nonlinear state-action features for value estimation. Across five continuous-control benchmarks, SR-HOM outperforms parameter-matched multilayer-perceptron baselines, including a \(4.4\times\) improvement in sample efficiency and a \(74.0\%\) increase in best 100-episode moving-average return for LunarLanderContinuous-v3. Applied to online calibration of drifted tunable-coupler CZ and iSWAP gates for transmon qubits, simulations show it restores fidelities to \(0.9917\) and \(0.9952\) respectively, exceeding \(99.8\%\) of their drift-free calibrated values.

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