The Role of Entanglement in Quantum Reservoir Computing with Coupled Kerr Nonlinear Oscillators

TL;DR

This paper investigates how entanglement influences the performance of quantum reservoir computing using coupled Kerr oscillators for time-series prediction, highlighting optimal moderate entanglement levels.

Contribution

It demonstrates that moderate entanglement correlates with optimal predictive performance in a quantum reservoir system based on Kerr oscillators.

Findings

Optimal performance occurs at moderate, non-zero entanglement levels.

Moderate entanglement is consistently linked to better average predictive accuracy.

Higher dissipation rates can enhance the reservoir's predictive performance.

Abstract

Quantum Reservoir Computing (QRC) uses quantum dynamics to efficiently process temporal data. In this work, we investigate a QRC framework based on two coupled Kerr nonlinear oscillators, a system well-suited for time-series prediction tasks due to its complex nonlinear interactions and potentially high-dimensional state space. We explore how its performance in forecasting both linear and nonlinear time-series depends on key physical parameters: input drive strength, Kerr nonlinearity, and oscillator coupling, and analyze the role of entanglement in improving the reservoir's computational performance, focusing on its effect on predicting non-trivial time series. Using logarithmic negativity to quantify entanglement and normalized root mean square error (NRMSE) to evaluate predictive accuracy, individual parameter sweeps show that optimal performance occurs at moderate but non-zero entanglement. Furthermore, an aggregated binned analysis reveals that this moderate entanglement is consistently associated with the optimal average predictive performance across the parameter space, an observation that persists up to a threshold in the input frequency. This relationship persists under some levels of dissipation and dephasing. In particular, we find that higher dissipation rates can enhance performance. These findings contribute to the broader understanding of quantum reservoirs for high performance, efficient quantum machine learning and time-series forecasting.