Learning Photonic Dynamics for Higher-Order Associative Memory

Photonic hardware is usually used to accelerate fixed neural operators. We ask whether a photonic feedback loop can instead be trained as part of the neural model. We study associative memory—recovering a stored pattern from a corrupted cue—with recurrent photonic dynamics. Programmable optical projections and nonlinear interactions repeatedly update an internal state. Higher-order interactions reduce interference among stored memories, and a readout based on the stored memories restores information lost by normalization. At the highest tested load of 128 stored memories, a model trained for four feedback steps reconstructs 5.3 dB better at four steps than after running to convergence. Thus, more convergence can make the task result worse: runtime itself becomes part of what the model learns. Controlled retrieval shows when higher-order interactions help, while a real multi-hop retrieval test shows they can also hurt generalization. Finally, we map the model to a hybrid photonic/electronic circuit and simulate finite precision, loss, drift, and calibration. These hardware results are simulations, not measured device performance.