Recent advancements in artificial neural networks have enabled impressive tasks on classical computers, but they demand significant computational resources. While quantum computing offers potential beyond classical systems, the advantages of quantum neural networks (QNNs) remain largely unexplored. In this work, we present and examine a quantum circuit (QC) that implements and aims to improve upon the classical echo-state network (ESN), a type of reservoir-based recurrent neural networks (RNNs), using quantum computers. Typically, ESNs consist of an extremely large reservoir that learns high-dimensional embeddings, enabling prediction of complex system trajectories. Quantum echo-state networks (QESNs) aim to reduce this need for prohibitively large reservoirs by leveraging the unique capabilities of quantum computers, potentially allowing for more efficient and higher performing time-series prediction algorithms. The proposed QESN can be implemented on any digital quantum computer implementing a universal gate set, and does not require any sort of stopping or re-initialization of the circuit, allowing continuous evolution of the quantum state over long time horizons. We conducted simulated QC experiments on the chaotic Lorenz system, both with noisy and noiseless models, to demonstrate the circuit's performance and its potential for execution on noisy intermediate-scale quantum (NISQ) computers.
Quantum Observers: A NISQ Hardware Demonstration of Chaotic State Prediction Using Quantum Echo-state Networks
University of South Carolina - Columbia（南卡罗来纳大学哥伦比亚分校）；Naval Surface Warfare Center, Panama City Division（海军水面 warfare 中心，巴拿马城分部）；RTX BBN, Cambridge MA（RTX BBN，马萨诸塞州剑桥）
Butterfly Echo Protocol for Axis-Agnostic Heisenberg-Limited Metrology
United States Naval Academy（美国海军学院）；Harvard University（哈佛大学）；Massachusetts Institute of Technology（麻省理工学院）；The College of William & Mary（威廉玛丽学院）；NIST/University of Maryland（美国国家标准与技术研究院/马里兰大学）
Quantum reservoir computing for predicting and characterizing chaotic maps
University of Electronic Science and Technology of China（电子科技大学）；Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China（电子科技大学基础与前沿研究院）；Key Laboratory of Quantum Physics and Photonic Quantum Information, Ministry of Education, University of Electronic Science and Technology of China（电子科技大学量子物理与光子量子信息教育部重点实验室）；School of Life Science and Technology, University of Electronic Science and Technology of China（电子科技大学生命科学与技术学院）；Institute of Science Tokyo（东京科学大学）；Center for Mind/Brain Sciences (CIMeC), University of Trento（特伦托大学心智/脑科学中心）；Shimmer Center, Tianfu Jiangxi Laboratory（天府江西实验室闪烁中心）
Driven chaotic mesoscopic systems, dissipation and decoherence
受驱动混沌系统在介观物理学、核物理、原子物理和分子物理中都有重要的研究意义。这样的系统[坐标$(Q,P)$ ] $ tend to absorb energy. This irreversible effect is known as dissipation. "Driving" means that a parameter $ x $ is changed in time. More generally, $ x $ may be a dynamical variable. In such case the interaction of $（x，p）$ with the environmental degrees of freedom $（Q，P）$导致退相以及耗散。我们介绍了一个一般框架的耗散和退相的分析，我们澄清了紧密的联系，最近的研究量子不可逆性（也被称为“Loschirtecho”或“保真度”的量子计算）。具体的模型系统，将被提出：粒子在一个盒子里移动的墙壁驱动，和粒子在一个盒子/环驱动的电动势。这两个例子与核摩擦和介观电导的研究有关。
