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  • Open Access

Memory-enhanced quantum extreme learning machines for characterizing non-Markovian dynamics

Hajar Assil1,*, Abderrahim El Allati1,2, and Gian Luca Giorgi3,†

  • *Contact author: assil.hajar@etu.uae.ac.ma
  • †Contact author: gianluca@ifisc.uib-csic.es
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Phys. Rev. Research 8, 023306 – Published 17 June, 2026

DOI: https://doi.org/10.1103/ycvz-lzgl

Abstract

We use a quantum extreme learning machine (QELM) for characterizing and estimating parameters of quantum dynamics generated by a tunable collision model. The input to the learning protocol consists of quantum states produced by successive system-environment interactions, while the reservoir is implemented as a disordered many-body quantum system evolving under a fixed Hamiltonian. We systematically explore how extending the QELM feature space, through the inclusion of temporal information and additional observables, affects estimation performance. Our results demonstrate that temporal extensions of the feature vector consistently and significantly enhance estimation accuracy relative to the baseline protocol. Notably, incorporating memory from earlier time steps yields the most substantial and robust improvements, whereas extensions based solely on additional observables offer only marginal gains. Crucially, the advantage conferred by temporal memory becomes increasingly pronounced as the dynamics become more strongly non-Markovian, indicating that environmental memory effects serve as a constructive resource for learning.

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Phys. Rev. Research 8, 023306– Published 17 June, 2026
  • Received 4 March 2026
  • Accepted 1 June 2026
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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