Laser interferometry as a robust neuromorphic platform for machine learning

ArXi:2601.18047v4 Announce Type: replace-cross 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 straightforward experimental implementation compared to previous proposals for, and nstrations of, $\textit{in situ}$ inference. Beyond $\textit{in situ}$ inference, the method enables $\textit{in situ.