标题： 解开光子储备池：通过在拉伸域上的随机傅里叶编码增强表达能力 显示英文标题

标题： Unwrapping photonic reservoirs: enhanced expressivity via random Fourier encoding over stretched domains

摘要： 光子 reservoir 计算（RC）系统利用光学波的复杂传播和非线性相互作用来执行信息处理任务。这些系统采用光数据编码（在场振幅和/或相位上）、随机散射和非线性检测的组合，以生成可通过线性读出层处理的非线性特征。在这项工作中，我们提出了一种新颖的散射辅助光子 reservoir 编码方案，在该方案中，输入相位被故意多次包裹在光学波的自然周期 $[0,2\pi)$ 之外。我们证明了，与通过双射性损失阻碍非线性可分性相反，包裹显著提高了 reservoir 在回归和分类任务中的预测性能，这些任务在标准 $2\pi$ 周期内无法实现。我们还证明了这种反直觉的效果源于由编码引入的随机合成频率集合之间的非线性干涉，这生成了一个丰富的特征空间，跨越数据的特征维度和样本维度。我们的结果强调了在基于相位编码的 RC 系统中，经过工程设计的相位包裹作为计算资源的潜力，为基于拓扑和几何拉伸设计和优化物理计算平台开辟了新的途径。 显示英文摘要

摘要： Photonic Reservoir Computing (RC) systems leverage the complex propagation and nonlinear interaction of optical waves to perform information processing tasks. These systems employ a combination of optical data encoding (in the field amplitude and/or phase), random scattering, and nonlinear detection to generate nonlinear features that can be processed via a linear readout layer. In this work, we propose a novel scattering-assisted photonic reservoir encoding scheme where the input phase is deliberately wrapped multiple times beyond the natural period of the optical waves $[0,2\pi)$. We demonstrate that, rather than hindering nonlinear separability through loss of bijectivity, wrapping significantly improves the reservoir's prediction performance across regression and classification tasks that are unattainable within the canonical $2\pi$ period. We demonstrate that this counterintuitive effect stems from the nonlinear interference between sets of random synthetic frequencies introduced by the encoding, which generates a rich feature space spanning both the feature and sample dimensions of the data. Our results highlight the potential of engineered phase wrapping as a computational resource in RC systems based on phase encoding, paving the way for novel approaches to designing and optimizing physical computing platforms based on topological and geometric stretching.