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  • Open Access

Quantum reservoir computing for predicting and characterizing chaotic maps

Qingyu Li1,2,*, Chiranjib Mukhopadhyay1,2, Ludovico Minati3,4,5, and Abolfazl Bayat1,2,6,†

  • *Contact author: lqy859405@gmail.com
  • †Contact author: abolfazl.bayat@uestc.edu.cn
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Phys. Rev. Research 8, 013304 – Published 19 March, 2026

DOI: https://doi.org/10.1103/qwvv-xqfq

Abstract

Quantum reservoir computing has emerged as a promising paradigm for harnessing quantum systems to process temporal data efficiently by bypassing the costly training of gradient-based learning methods. Here, we demonstrate the capability of this approach to predict and characterize chaotic dynamics in discrete nonlinear maps, exemplified through the logistic and Hénon maps. While achieving excellent predictive accuracy, we also demonstrate the optimization of training hyperparameters of the quantum reservoir based on the properties of the underlying map, thus paving the way for efficient forecasting with other discrete and continuous time-series data. Using closed-loop prediction of distant future steps, our protocol discriminates between chaotic and nonchaotic phases without prior knowledge of the underlying map or the nature of the time series. Furthermore, the framework exhibits robustness against decoherence when trained in situ and shows insensitivity to reservoir Hamiltonian variations as well as robustness to finite-sampling error. These results highlight quantum reservoir computing as a scalable and noise-resilient tool for modeling complex dynamical systems, with immediate applicability in near-term quantum hardware.

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Phys. Rev. Research 8, 013304– Published 19 March, 2026
  • Received 22 September 2025
  • Accepted 17 February 2026
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