Optimization of the Memory Reset Rate of a Quantum Echo-State Network for Time Sequential Tasks

TL;DR

This paper investigates how the memory reset rate affects the performance of quantum echo-state networks in time-series prediction, demonstrating that optimal reset rates significantly improve accuracy on quantum hardware.

Contribution

It introduces an analysis of the memory reset rate's impact on quantum reservoir computing and demonstrates optimized reset rates enhance predictive accuracy.

Findings

Memory capacity peaks at reset rates within [0,1]

Memory capacity scales linearly with qubit number

Optimized reset rates reduce mean squared error by ~80%

Abstract

Quantum reservoir computing is a class of quantum machine learning algorithms involving a reservoir of an echo state network based on a register of qubits, but the dependence of its memory capacity on the hyperparameters is still rather unclear. In order to maximize its accuracy in time--series predictive tasks, we investigate the relation between the memory of the network and the reset rate of the evolution of the quantum reservoir. We benchmark the network performance by three non--linear maps with fading memory on IBM quantum hardware. The memory capacity of the quantum reservoir is maximized for central values of the memory reset rate in the interval [0,1]. As expected, the memory capacity increases approximately linearly with the number of qubits. After optimization of the memory reset rate, the mean squared errors of the predicted outputs in the tasks may decrease by a factor ~1/5 with respect to previous implementations.