用线性光学实现抗损神经网络，支持就地训练与推理。
Laser interferometry as a robust neuromorphic platform for machine learning
- 仅用光场位移和干涉实现非线性，简化实验部署。
- 在光子损耗下仍保持稳定性能，鲁棒性强。
- 适合光子芯片上部署的机器学习系统研究者。
我们提出一种仅使用线性光学资源（光场位移和干涉）实现光学神经网络的方法，通过将输入编码为相位偏移来实现神经网络所需的非线性，相比以往的原位推理方案更易于实验实现。该方法不仅支持原位推理，还可通过参数移位法或物理反向传播等成熟技术，直接从线性光学电路测量中提取梯度，实现原位训练。我们还研究了光子损耗的影响，发现该模型对损耗具有很强的鲁棒性。
原文摘要 · Abstract (English)
We present a method for implementing an optical neural network using only linear optical resources, namely field displacement and interferometry applied to coherent states of light. The nonlinearity required for learning in a neural network is realized via an encoding of the input into phase shifts allowing for far more straightforward experimental implementation compared to previous proposals for, and demonstrations of, $\textit{in situ}$ inference. Beyond $\textit{in situ}$ inference, the method enables $\textit{in situ}$ training by utilizing established techniques like parameter shift methods or physical backpropagation to extract gradients directly from measurements of the linear optical circuit. We also investigate the effect of photon losses and find the model to be very resilient to these.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。