AI 中文总结
该研究针对混合推理系统，明确了线性光学前端的计算目标，揭示非局域相干光学系统可提升分类任务性能，为光电混合推理系统提供了设计准则。
AI 中文摘要
将光学前端与数字后端配对的混合推理系统为将计算任务卸载到物理层提供了途径，但光学应执行何种计算以及何时该方案具备优势仍不明确。针对分类任务，本文表明，设计良好的光学前端可重塑传感器强度读出的统计特性，以提升类间可分性，该特性由Bhattacharyya距离量化。这一无需训练的度量指标可预测下游准确率，并揭示判别信息大多存在于像素间相关性中。随后，本文明确了相干性与不同形式非局域性的作用：由于传感器测量强度，线性前端生成的特征为输入场的二次项，但仅非局域相干光学系统可利用相关信息，这类系统可产生显著性能提升，超越经最优训练的线性预处理器。上述结果为光电混合推理系统提供了物理洞见与新设计准则。
英文摘要
Hybrid inference systems that pair an optical frontend with a digital backend offer a route to offload computation to the physical layer. Yet what the optics should compute, and when this is beneficial, has remained unclear. Here, we show that, for classification tasks, a well-designed optical frontend reshapes the statistics of the sensor intensity readout to improve class separability, quantified by the Bhattacharyya distance. This training-free metric predicts downstream accuracy and reveals that much of the discriminative information resides in inter-pixel correlations. We then identify the roles of coherence and different forms of nonlocality. Because the sensor measures intensity, a linear frontend produces features that are quadratic in the input field; however, only nonlocal, coherent optical systems can exploit the associated information. Such systems can yield significant performance gains, surpassing the best trained linear preprocessor. These results provide physical insights and new design principles for optimal optical--electronic inference systems.
