Quantum Reservoir Computing for Optimal Control

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper investigates the application of quantum reservoir computing (QRC) to the problem of optimal control within complex dynamical systems. Traditional optimal control methods often struggle with systems exhibiting high dimensionality, nonlinearities, and time-varying dynamics. Quantum systems, due to their inherent sensitivity to initial conditions and their ability to exist in superpositions, offer a potentially powerful alternative. This work proposes a framework where a continuously-operated quantum system, such as a driven cavity QED system, acts as a reservoir, mapping the system's dynamics onto a quantum state space. A variational quantum algorithm is then employed to learn the optimal control policy directly from the reservoir's evolved quantum state. We demonstrate, theoretically, that this approach can achieve optimal or near-optimal control performance, offering a novel method for tackling control problems where classical techniques fall short. The core of this approach lies in the reservoir's ability to effectively encode the system's dynamics and the variational algorithm's capacity to extract the desired control policy. The key mathematical elements explored include the mapping of system dynamics to the quantum state space, the formulation of the variational algorithm, and the analysis of the control performance metrics. This research represents a significant step towards utilizing the unique properties of quantum systems for advanced control applications.

DOI: 10.5281/zenodo.22316271

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