Photonic reservoir computing with dimensionally compressed readout

2026-09-07

AI Summary

本文针对硬件限制下的读出层尺寸问题，采用随机投影方法压缩高维水库状态，评估并展示了该方法在特定压缩范围内的优越性能。

Abstract

This work addresses a hardware constraint in reservoir computing: the limited size of the readout layer imposed by systems with a physical readout. We investigate a strategy to accommodate this constraint based on random projection, which compresses high-dimensional reservoir states into a lower-dimensional subspace while preserving key properties of the source space and information- processing capabilities. To evaluate this approach, we compare a small, standalone time delay reservoir against a larger configuration whose output is projected down to match the same restricted readout dimension. Using task-independent metrics, we demonstrate that the distribution of information-processing capacities may differ between the two configurations, even at identical readout sizes. Furthermore, we perform a comprehensive hyperparameter scan to assess how both systems behave under varying physical regimes. Finally, we benchmark this approach on the standard NARMA10 task, showing that the random projection framework can yield superior performance compared to a standalone constrained reservoir, within specific compression range. These results provide a scalable pathway to bypass physical readout bottlenecks in hardware-based reservoir computing.

Problem

• reservoir computing
• readout layer
• hardware constraint
• random projection
• dimensional compression

Innovation

• random projection
• dimensional compression
• reservoir computing
• readout layer
• information processing capacity