Practical and Scalable Quantum Reservoir Computing

Chuanzhou Zhu, Peter J. Ehlers, Hendra I. Nurdin, Daniel Soh
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引用次数: 0

Abstract

Quantum Reservoir Computing leverages quantum systems to solve complex computational tasks with unprecedented efficiency and reduced energy consumption. This paper presents a novel QRC framework utilizing a quantum optical reservoir composed of two-level atoms within a single-mode optical cavity. Employing the Jaynes-Cummings and Tavis-Cummings models, we introduce a scalable and practically measurable reservoir that outperforms traditional classical reservoir computing in both memory retention and nonlinear data processing. We evaluate the reservoir's performance through two primary tasks: the prediction of time-series data via the Mackey-Glass task and the classification of sine-square waveforms. Our results demonstrate significant enhancements in performance with increased numbers of atoms, supported by non-destructive, continuous quantum measurements and polynomial regression techniques. This study confirms the potential of QRC to offer a scalable and efficient solution for advanced computational challenges, marking a significant step forward in the integration of quantum physics with machine learning technology.
实用且可扩展的量子储层计算
量子存储计算(Quantum Reservoir Computing)利用量子系统以前所未有的效率和更低的能耗解决复杂的计算任务。本文提出了一种新颖的 QRC 框架,利用单模光腔内由两级原子组成的量子光库。利用杰恩斯-康明斯和塔维斯-康明斯模型,我们介绍了可升级和实际可测量的贮存器,它在内存保留和非线性数据处理方面都优于传统的经典贮存器计算。我们通过两个主要任务来评估蓄水池的性能:通过 Mackey-Glass 任务预测时间序列数据和正弦波形分类。我们的结果表明,在非破坏性、连续量子测量和多项式回归技术的支持下,随着原子数量的增加,性能得到了显著提高。这项研究证实了 QRC 在为高级计算挑战提供可扩展的高效解决方案方面的潜力,标志着量子物理与机器学习技术的整合向前迈出了重要一步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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