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Over the last decade, researchers have studied the synergy between quantumcomputing (QC) and classical machine learning (ML) algorithms. However,measurements in QC often disturb or destroy quantum states, requiring multiplerepetitions of data processing to estimate observable values. In particular,this prevents online (i.e., real-time, single-shot) processing of temporal dataas measurements are commonly performed during intermediate stages. Recently, itwas proposed to sidestep this issue by focusing on tasks with quantum output,thereby removing the need for detectors. Inspired by reservoir computers, amodel was proposed where only a subset of the internal parameters are optimizedwhile keeping the others fixed at random values. Here, we also process quantumtime series, but we do so using a quantum optical recurrent neural network(QORNN) of which all internal interactions can be trained. As expected, thisapproach yields higher performance, as long as training the QORNN is feasible.We further show that our model can enhance the transmission rate of quantumchannels that experience certain memory effects. Moreover, it can counteractsimilar memory effects if they are unwanted, a task that could previously onlybe solved when redundantly encoded input signals were available. Finally, werun a small-scale version of this last task on the photonic processor Borealis,demonstrating that our QORNN can be constructed using currently existinghardware.

A Quantum Optical Recurrent Neural Network for Online Processing of Quantum Times Series