AI 中文总结
该研究探究量子储备池计算（QRC）在神经动力学预测中的表现，构建基于横场伊辛模型的量子储备池，在基准任务与模拟EEG数据上验证其可行性，为量子系统用于临床时间序列预测建立了实用基线。
AI 中文摘要
从短时长记录中预测神经活动仍是一项基础挑战。储备池计算或可为时间预测提供高效范式，但经典储备池在小数据场景下通常表现不佳。本文探究量子储备池计算（QRC）是否可帮助克服这一局限。基于近期进展，我们引入一种基于横场伊辛模型的量子储备池，结合异构量子测量与多项式岭回归。在标准基准任务上，结果显示该量子储备池整体优于经典对应模型，且预测精度强烈依赖储备池参数。我们进一步通过在量子硬件上运行相同任务验证可行性。为评估其在生物信号上的性能，我们采用并行储备池架构在模拟人类脑电图（EEG）数据上测试QRC。在这一具有挑战性的任务中，被测量子储备池未达到经典模型的性能，但生成了稳定、收敛的预测结果。这是使用量子储备池预测生物现实神经数据的重要第一步。总体而言，我们的发现表明，尽管当前量子硬件与并行储备池架构在复杂神经信号上尚未超越经典方法，但QRC可在近期设备上执行，且确实能在类EEG的现实数据上收敛。本工作为未来旨在用量子系统进行临床时间序列预测的算法与硬件开发建立了实用基线。
英文摘要
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.
Comments7 pages (including references), 2 figures, Accepted at IEEE Quantum Week (QCE 2026) short technical paper, Applications category
Journal refProc. IEEE Int. Conf. Quantum Comput. Eng. (QCE), 2026
