Abstract:We investigate quantum reservoir computing (QRC) using a hybrid qubit-boson system described by the Jaynes-Cummings (JC) Hamiltonian and its dispersive limit (DJC). These models provide high-dimensional Hilbert spaces and intrinsic nonlinear dynamics, making them powerful substrates for temporal information processing. We systematically benchmark both reservoirs through linear and nonlinear memory tasks, demonstrating that they exhibit an unusual superior nonlinear over linear memory capacity. We further test their predictive performance on the Mackey-Glass time series, a widely used benchmark for chaotic dynamics, and show comparable forecasting ability. We also investigate how memory and prediction accuracy vary with reservoir parameters, and show the role of higher-order bosonic observables and time multiplexing in enhancing expressivity, even in minimal spin-boson configurations. Our results establish JC- and DJC-based reservoirs as versatile platforms for time-series processing and as elementary units that overcome the setting of equivalent qubit pairs and offer pathways toward tunable, high-performance quantum machine learning architectures.
| Comments: | 16 pages, 14 figures, published version |
| Subjects: | Quantum Physics (quant-ph); Machine Learning (cs.LG) |
| Cite as: | arXiv:2510.00171 [quant-ph] |
| (or arXiv:2510.00171v2 [quant-ph] for this version) | |
| https://doi.org/10.48550/arXiv.2510.00171 arXiv-issued DOI via DataCite |
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| Journal reference: | Phys. Rev. Research 8, 023148 (2026) |
| Related DOI: | https://doi.org/10.1103/ffd3-ytbt
DOI(s) linking to related resources |
From: Sreetama Das [view email]
[v1]
Tue, 30 Sep 2025 18:46:20 UTC (2,451 KB)
[v2]
Tue, 19 May 2026 23:35:33 UTC (2,460 KB)
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