用量子谐振子-比特系统实现时间序列预测，展现非线性记忆优势。
Quantum reservoir computing in Jaynes-Cummings models: Nonlinear memory and time-series prediction
- 基于杰恩斯-克林顿模型构建量子储池，利用非线性动力学处理时序信息。
- 在混沌时间序列上表现接近经典方法，非线性记忆能力优于线性记忆。
- 小规模系统通过高阶光子可观测量和时间复用提升表达能力，适合量子机器学习。
我们研究基于杰恩斯-克林顿（JC）哈密顿量及其色散极限（DJC）的量子储池计算（QRC），该系统为量子比特-玻色子混合体系，具有高维希尔伯特空间和内在非线性动力学，适用于时序信息处理。通过线性和非线性记忆任务系统评估两种储池性能，结果表明其表现出异常优越的非线性记忆能力。进一步在广泛使用的混沌时间序列基准Mackey-Glass上测试预测性能，结果显示其预测能力相当。同时研究了储池参数对记忆与预测精度的影响，揭示高阶玻色子可观测量和时间复用在增强表达能力中的作用，即使在最小自旋-玻色子配置下亦可实现。结果确立了基于JC和DJC的储池作为多功能时序处理平台的潜力，并为可调、高性能量子机器学习架构提供了路径。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。