aiqbench analysis
This study demonstrates the feasibility of executing quantum reservoir computing on near-term hardware to forecast neural activity, showing superior performance on standard benchmarks compared to classical counterparts. While simulations with realistic EEG data did not yet surpass classical methods in accuracy, they confirmed stable convergence, establishing a practical baseline for future clinical time-series forecasting applications. The work highlights that while current devices are limited by noise and architecture constraints, QRC remains a viable pathway for leveraging quantum advantages in neurodynamical modeling as hardware matures.
A Quantum Reservoir for Neurodynamical Forecasting
arXiv:2608.00139v1 Announce Type: new Abstract: Forecasting neural activity from short recordings remains a fundamental challenge. Reservoir computing may offer an efficient paradigm for temporal prediction, however classical reservoirs typically underperform in small-data regimes. Here we investigate whether quantum reservoir computing (QRC) can help overcome this limitation. Building on recent advances, we introduce a quantum reservoir based on a transverse-field Ising model, combined with heterogeneous quantum measurements and polynomial ridge regression. On a standard benchmark task, results show that the quantum reservoir outperforms a classical counterpart overall, with prediction accuracy strongly dependent on reservoir parameters. We further demonstrate feasibility by running the same task on quantum hardware. To assess performance on biological signals, we evaluate QRC on simulated human electroencephalography (EEG) data with a parallel reservoir architecture. On this challenging task, the tested quantum reservoir did not match the performance of the classical one, but it produced stable, convergent predictions. This is a meaningful first step toward forecasting of biologically realistic neural data using a quantum reservoir. Overall, our findings indicate that although current quantum hardware and parallel reservoir architectures do not yet surpass classical methods on complex neural signals, QRC can be executed on near-term devices and does converge with realistic EEG-like data. This work establishes a practical baseline for future algorithmic and hardware developments aimed at clinical time-series forecasting with quantum systems.
Abstract:Forecasting neural activity from short recordings remains a fundamental challenge. Reservoir computing may offer an efficient paradigm for temporal prediction, however classical reservoirs typically underperform in small-data regimes. Here we investigate whether quantum reservoir computing (QRC) can help overcome this limitation. Building on recent advances, we introduce a quantum reservoir based on a transverse-field Ising model, combined with heterogeneous quantum measurements and polynomial ridge regression. On a standard benchmark task, results show that the quantum reservoir outperforms a classical counterpart overall, with prediction accuracy strongly dependent on reservoir parameters. We further demonstrate feasibility by running the same task on quantum hardware. To assess performance on biological signals, we evaluate QRC on simulated human electroencephalography (EEG) data with a parallel reservoir architecture. On this challenging task, the tested quantum reservoir did not match the performance of the classical one, but it produced stable, convergent predictions. This is a meaningful first step toward forecasting of biologically realistic neural data using a quantum reservoir. Overall, our findings indicate that although current quantum hardware and parallel reservoir architectures do not yet surpass classical methods on complex neural signals, QRC can be executed on near-term devices and does converge with realistic EEG-like data. This work establishes a practical baseline for future algorithmic and hardware developments aimed at clinical time-series forecasting with quantum systems.
Quantum Physics (quant-ph); Quantitative Methods (q-bio.QM)
arXiv:2608.00139 [quant-ph]
(or arXiv:2608.00139v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2608.00139 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
Proc. IEEE Int. Conf. Quantum Comput. Eng. (QCE), 2026
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Full text under the publisher's open licence · 324 words
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