发表机构
Tulane University; Dongduk Women’s University; SSJDWSSSS Government Post Graduate College（杜兰大学; 同德女子大学; SSJDWSSSS政府研究生学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出耗散量子储备计算（DQRC）框架，利用固定单量子比特储备池从数据学习输入-输出映射，构建量子动力系统的数字孪生，在HHG基准上匹配或超越现有模型，并展现跨驱动泛化能力。
AI 中文摘要
从输入-输出数据对受驱动的多体量子系统的响应进行建模是困难的：其动力学是非线性的、历史依赖的，并且随着系统规模的增长，模拟成本高昂。一个典型的例子是高次谐波产生（HHG），其中强场驱动介质发射对驱动高度敏感并编码长程时间关联的辐射。我们引入了一个耗散量子储备计算（DQRC）框架，为这类系统构建数字孪生，直接从数据中学习其输入-输出映射，而储备池本身——一个小型开放量子系统——保持固定，仅训练经典读出层。我们证明，一个最小的单量子比特储备池能够再现一个规模大得多的伊辛自旋链的HHG响应，并在代表性基准上匹配甚至在某些指标上超越先前报道的时间卷积和Kolmogorov-Arnold网络模型，同时使用更简单、物理上可实现的系统。一个固定的储备池还能在广泛的驱动范围内泛化，表明它学习的是共享的物理响应结构，而非记忆轨迹。这些结果确立了耗散量子储备池作为非线性、记忆依赖量子动力学的紧凑、物理基础的数字孪生。代码可在 \ref{this https URL}{this https URL} 获取。
英文摘要
Modeling the response of driven many-body quantum systems from input--output data is difficult: the dynamics are nonlinear, history dependent, and expensive to simulate as system size grows. A paradigmatic case is High-Harmonic Generation~(HHG), where a strong field drives a medium to emit radiation that is highly sensitive to the drive and encodes long-range temporal correlations. We introduce a dissipative quantum reservoir computing~(DQRC) framework that builds a digital twin of such a system, learning its input--output map directly from data while the reservoir---itself a small open quantum system---stays fixed and only a classical readout is trained. We show that a minimal single-qubit reservoir reproduces the HHG response of a substantially larger Ising spin chain, and on a representative benchmark matches and on several metrics surpasses previously reported temporal convolutional and Kolmogorov--Arnold-network models, while using a simpler, physically realizable system. A single fixed reservoir further generalizes across a broad range of drives, indicating that it learns a shared physical response structure rather than memorizing trajectories. These results establish dissipative quantum reservoirs as compact, physically grounded digital twins for nonlinear, memory-dependent quantum dynamics. Code is available at \href{https://github.com/AI-and-Quantum-Computing/DQuRC}{https://github.com/AI-and-Quantum-Computing/DQuRC}.
Comments13 pages, 9 figures
