非线性光子储能器中的稳定性边缘：激子极化子计算的通用设计原理
Edge of Stability in Nonlinear Photonic Reservoirs: Universal Design Principle for Exciton-Polariton Computing
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中文总结 AI 辅助
研究基于二维离散复金兹堡-朗道方程的储层计算系统用于手写字母识别，通过参数扫描和动力学分析发现稳定性边缘附近计算性能最佳，还在树莓派5上验证可行性，为优化物理储层计算硬件提供设计原理。
中文摘要 AI 辅助
储层计算已成为一种利用物理系统固有动力学进行复杂信息处理的强大范式。在此，我们从理论和数值上研究了基于二维离散复金兹堡-朗道方程的储层计算系统，该方程是描述驱动耗散激子极化子晶格的基本模型。我们将此框架应用于具有挑战性的52类手写字母识别任务，分解时间输入信号以模拟单词识别。我们的系统实现了高测试准确率，显著优于线性基线。通过系统的参数扫描和非线性动力学分析，我们建立了计算性能与基础物理之间的定量联系：最优分类出现在稳定性边缘附近，此时最大李雅普诺夫指数接近零。该原理在不同控制参数（包括非线性耦合和线性增益）中普遍成立。此外，我们通过在树莓派5边缘设备上部署训练模型来验证该方法的实际可行性。这些结果不仅证明了极化子晶格用于神经形态计算的潜力，还为优化物理储层计算硬件提供了一般设计原理。
英文摘要
Reservoir computing has emerged as a powerful paradigm for harnessing the intrinsic dynamics of physical systems to perform complex information processing. Here, we theoretically and numerically investigate a reservoir computing system based on the two-dimensional discrete complex Ginzburg-Landau equation, a fundamental model describing driven-dissipative exciton-polariton lattices. We apply this framework to the challenging 52-class handwritten letter recognition task, decomposing temporal input signals to simulate word recognition. Our system achieves a high test accuracy, significantly outperforming a linear baseline. Through systematic parameter scans and nonlinear dynamics analysis, we establish a quantitative link between computational performance and the underlying physics: optimal classification occurs near the edge of stability, where the maximum Lyapunov exponent approaches zero. This principle holds universally across different control parameters, including nonlinear coupling and linear gain. Furthermore, we validate the practical feasibility of this approach by deploying the trained model on a Raspberry Pi 5 edge device. These results not only demonstrate the potential of polariton lattices for neuromorphic computing but also provide a general design principle for optimizing physical reservoir computing hardware.
