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

Quantum Physics [Submitted on 17 Mar 2026] Memory-enhanced quantum extreme learning machines for characterizing non-Markovian dynamics Hajar Assil, Abderrahim El Allati, Gian Luca Giorgi We use a Quantum Extreme Learning Machine 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. Subjects: Quantum Physics (quant-ph); Computational Physics (physics.comp-ph) Cite as: arXiv:2603.17182 [quant-ph]   (or arXiv:2603.17182v1 [quant-ph] for this version)   https://doi.org/10.48550/arXiv.2603.17182 Focus to learn more Submission history From: Hajar Assil [view email] [v1] Tue, 17 Mar 2026 22:22:22 UTC (656 KB) Access Paper: HTML (experimental) view license Current browse context: quant-ph < prev   |   next > new | recent | 2026-03 Change to browse by: physics physics.comp-ph References & Citations INSPIRE HEP NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)