解耦时间复用光 reservoir computing 中的记忆与非线性
Disentangling Memory and Nonlinearity in Time-Multiplexed Optical Reservoir Computing
AI总结：
该研究解耦时间复用光 reservoir computing 的记忆与非线性，采用无源线性光子 reservoir，发现非耦合虚拟节点加二次非线性的 reservoir 可适配部分任务，瞬态耦合与延迟反馈能提升计算能力。
摘要：
我们分析并解耦了非线性、瞬态动力学和延迟反馈各自及共同的作用，研究这些机制如何满足光子 reservoir computing 中不同任务的需求。该研究采用无源线性光子 reservoir，其光-电接口处的光电二极管是唯一的非线性来源。我们通过显式计算和比较贡献单项式，量化不同任务的需求并与 reservoir 提供的动力学进行对比。研究发现，具有非耦合虚拟节点和二次非线性的 reservoir，已能在源自具有二次非线性的动力学系统（如 Lorenz63 系统）的任务中实现良好性能。此外，只要 reservoir 中存在任务所需的最低非线性，瞬态耦合和延迟反馈可通过有效的多步积分方案，补偿缺失的高阶单项式，显著提升计算能力。
英文摘要：
We analyze and disentangle the individual and combined roles of nonlinearity, transient dynamics, and delayed feedback, and investigate how these mechanisms contribute to different task requirements in photonic reservoir computing. This is achieved using a passive linear photonic reservoir in which a photodiode at the optical-to-electrical interface provides the sole source of nonlinearity. We demonstrate how the requirements of different tasks can be quantified and compared to the dynamics provided by the reservoir by explicitly calculating and comparing the contributing monomials. Our findings include that a reservoir with uncoupled virtual nodes and a quadratic nonlinearity can already achieve good performance for tasks derived from dynamical systems with quadratic nonlinearities, such as the Lorenz63 system. Furthermore, transient coupling and delayed feedback significantly enhance the computational capabilities by compensating for missing higher-order monomials through an effective multi-step integration scheme, given the minimum nonlinearity required for the task is present in the reservoir.
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