Laser interferometry as a robust neuromorphic platform for machine learning
This work provides a robust neuromorphic platform for machine learning, offering a more straightforward experimental implementation compared to previous optical neural network methods.
The authors tackled the problem of implementing optical neural networks by using linear optical resources like field displacement and interferometry, achieving resilience to photon losses and enabling both in situ inference and training.
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 rules 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.