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

TL;DR

This paper introduces a trainable quantum optical recurrent neural network (QORNN) capable of online processing of quantum time series, improving quantum channel transmission and counteracting memory effects, demonstrated on existing photonic hardware.

Contribution

It presents a fully trainable QORNN model for real-time quantum time series processing, advancing beyond reservoir computing approaches.

Findings

QORNN achieves higher performance in processing quantum time series.

The model enhances quantum channel transmission rates with memory effects.

Demonstrated feasibility on the Borealis photonic processor.

Abstract

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