发表机构
Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg; School of Computing, Engineering and Digital Technologies, Teesside University（安全、可靠性与信任跨学科中心（SnT），卢森堡大学; 计算、工程与数字技术学院，泰赛大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该综述围绕量子储层计算展开，构建通用系统模型组织其多方面内容，研究多种模拟平台等。虽当前结果未显示广泛量子优势，但明确了评估量子优势所需的资源核算、标准等，涵盖其基础、架构、应用等多方面进展与方向。
AI 中文摘要
量子储层计算（QRC）利用固定或弱调谐量子系统的动力学将时间和序列输入转换为测量特征，训练通常限于经典读出。这种分离减少了对重复量子参数更新的依赖，避免了与变分电路训练相关的贫瘠高原。其计算能力常归因于量子系统的指数大希尔伯特空间。然而，决定储层实际能计算什么的内存、非线性和表现力共同取决于输入编码、量子演化、可观测量、测量和读出，而非仅取决于希尔伯特空间维度。在硬件上，这些能力还受到有限采样、硬件噪声、测量反作用和估计可观测量成本的进一步限制，所以仅大状态空间并不能保证有用计算。在本综述中，我们开发了一个连接这些组件的通用系统模型，并用它来组织QRC基础、计算属性、储层架构、操作协议和物理实现。我们研究了自旋、光子、超导、玻色子、中性原子等模拟平台，以及应用、软件和高性能计算支持、基准测试和可重复性。分析区分了硬件演示和模拟，并确定了跨实现比较所依据的假设和资源。当前结果并未确立相对于匹配良好的经典储层的广泛量子优势。因此，我们指定了评估量子优势声明所需的资源核算、基准标准和理论标准。
英文摘要
Quantum reservoir computing (QRC) uses the dynamics of a fixed or weakly tuned quantum system to transform temporal and sequential inputs into measured features, while training is typically confined to a classical readout. This separation reduces reliance on repeated quantum parameter updates and avoids the barren plateaus associated with variational circuit training. Its computational power is often attributed to the exponentially large Hilbert space of the quantum system. However, the memory, nonlinearity, and expressivity that determine what a reservoir can actually compute depend jointly on the input encoding, quantum evolution, observables, measurement, and readout, not on Hilbert space dimension alone. On hardware, these capabilities are further constrained by finite sampling, hardware noise, measurement backaction, and the cost of estimating observables, so a large state space alone does not guarantee useful computation. In this survey, we develop a common system model that connects these components and use it to organize QRC foundations, computational properties, reservoir architectures, operating protocols, and physical implementations. We examine spin, photonic, superconducting, bosonic, neutral atom, and other analog platforms, together with applications, software and high performance computing support, benchmarking, and reproducibility. The analysis distinguishes hardware demonstrations from simulations and identifies the assumptions and resources that govern comparisons across implementations. Current results do not establish a broad quantum advantage over well matched classical reservoirs. We therefore specify the resource accounting, benchmark standards, and theoretical criteria needed to evaluate claims of quantum advantage.
