AI 中文总结
本文提出光谱分辨Hong-Ou-Mandel干涉架构，构建光学Actor-Critic智能体，在5个连续控制基准任务中性能优于多层感知器基线，还成功实现Transmon量子比特可调耦合门的在线校准，保真度恢复效果优异。
AI 中文摘要
基于Hong-Ou-Mandel（HOM）干涉的光学神经网络在基准学习任务上具有复杂度优势，但传统读出方式将符合计数谱压缩为单个标量，限制了其在连续动作强化学习等复杂场景的应用。本文提出光谱分辨HOM（SR-HOM）架构，将光子的光谱自由度提升为可训练的计算资源，用于构建紧凑的光学Actor-Critic智能体：对角光谱响应生成连续动作，高阶光谱关联为价值估计提供非线性状态-动作特征。在5个连续控制基准任务中，SR-HOM优于参数匹配的多层感知器基线，其中LunarLanderContinuous-v3任务的样本效率提升4.4倍，最佳100回合滑动平均回报提高74.0%；将其应用于Transmon量子比特的漂移可调耦合CZ和iSWAP门的在线校准，模拟显示其将保真度分别恢复至0.9917和0.9952，超过其无漂移校准值的99.8%。
英文摘要
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.
