Steinegger Joel, Räth Christoph
Institut für KI Sicherheit, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Wilhelm-Runge-Straße 10, 89081, Ulm, Germany.
Institut für Materialphysik im Weltraum, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Linder Höhe, 51170, Köln, Germany.
Sci Rep. 2025 Feb 20;15(1):6201. doi: 10.1038/s41598-025-87768-0.
Reservoir computing (RC) is among the most promising approaches for AI-based prediction models of complex systems. It combines superior prediction performance with very low CPU-needs for training. Recent results demonstrated that quantum systems are also well-suited as reservoirs in RC. Due to the exponential growth of the Hilbert space dimension obtained by increasing the number of quantum elements small quantum systems are already sufficient for time series prediction. Here, we demonstrate that three-dimensional systems can already well be predicted by quantum reservoir computing with a quantum reservoir consisting of the minimal number of qubits necessary for this task, namely four. This is achieved by optimizing the encoding of the data, using spatial and temporal multiplexing and recently developed read-out-schemes that also involve higher exponents of the reservoir response. We outline, test and validate our approach using eight prototypical three-dimensional chaotic systems. Both, the short-term prediction and the reproduction of the long-term system behavior (the system's "climate") are feasible with the same setup of optimized hyperparameters. Our results may be a further step towards the realization of a dedicated small quantum computer for prediction tasks in the NISQ-era.
Reservoir计算（RC）是基于人工智能的复杂系统预测模型中最具前景的方法之一。它将卓越的预测性能与极低的训练CPU需求相结合。最近的研究结果表明，量子系统也非常适合作为RC中的储层。由于通过增加量子元素数量获得的希尔伯特空间维度呈指数增长，小型量子系统已经足以进行时间序列预测。在此，我们证明，使用由完成此任务所需的最少量子比特数（即四个）组成的量子储层，通过量子储层计算已经可以很好地预测三维系统。这是通过优化数据编码、使用空间和时间复用以及最近开发的读出方案（该方案还涉及储层响应的更高指数）来实现的。我们使用八个典型的三维混沌系统概述、测试并验证了我们的方法。在相同的优化超参数设置下，短期预测和长期系统行为（系统的“气候”）的再现都是可行的。我们的结果可能是朝着在NISQ时代实现用于预测任务的专用小型量子计算机迈出的又一步。
