发表机构
Savitribai Phule Pune University（萨维特里巴伊·普勒浦那大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对非平衡动力系统相变检测问题，提出新型混合量子储层计算(nHQRC)框架，通过特定模型投影、优化技术及量子态跟踪构建轨迹预测，相比经典基准显著提高效率并减少轨迹衰减，建立近量子态检测蓝图。
AI 中文摘要
非平衡动力系统中高度非线性随机数据的分析需要能够在系统结构崩溃前检测潜在相变的计算框架。传统变分量子算法常受梯度消失、贫瘠高原问题和高昂训练开销的限制。本文提出一种新型混合量子储层计算(nHQRC)框架，它通过使用冻结、无序的横向场伊辛模型将时间相关随机驱动力投影到指数大的希尔伯特空间来绕过这些限制。为解决基线量子储层模型中存在的物理相位多缠绕漏洞，引入无前瞻、预放大流形缩放技术。多量子比特配置经遗传优化到“混沌边缘”，通过提取冯·诺依曼熵(S)和精确混合态量子费希尔信息(QFI)进行量子态跟踪以充当主要纠缠见证。利用这些量子触发器作为边界约束，通过生成性随机薛定谔桥(SSB)读出构建轨迹预测。与标准经典基准相比，该框架使系统漂移到扩散效率(η)显著提高，并使最大轨迹衰减(MTD)主动减少超13%。这为近期量子态检测和宏观子系统稳定建立了稳健的、O(1)时间开销蓝图。
英文摘要
The analysis of highly non-linear stochastic data within non-equilibrium dynamical systems requires computational frameworks capable of detecting latent phase transitions before systemic structural breakdowns occur. Traditional Variational Quantum Algorithms (VQAs) are frequently bottlenecked by vanishing gradients, the barren plateau problem, and prohibitive training overheads. In this paper, we propose a novel Hybrid Quantum Reservoir Computing (nHQRC) framework, which bypasses these limitations by employing a frozen, disordered Transverse-Field Ising Model (TFIM) to project time-dependent stochastic driving forces into an exponentially large Hilbert space. To resolve the physical phase multi-wrapping vulnerabilities present in baseline quantum reservoir models, we introduce a lookahead-free, pre-amplification manifold scaling technique. Multi-qubit configurations are genetically optimized to the "edge of chaos," while quantum state tracking is performed by extracting von Neumann entropy ($S$) and exact mixed-state Quantum Fisher Information (QFI) to act as leading entanglement witnesses. Utilizing these quantum triggers as boundary constraints, trajectory predictions are constructed via a generative Stochastic Schrödinger Bridge (SSB) readout. By subjecting the quantum reservoir to an 8-dimensional non-stationary stochastic driving field, the framework significantly improves systemic drift-to-diffusion efficiency ($η$) and actively arrests maximum trajectory decay (MTD) by over 13% compared to standard classical benchmarks. This establishes a robust, $\mathcal{O}(1)$ temporal overhead blueprint for near-term quantum regime detection and macroscopic subsystem stabilization.
Comments9 pages, 7 figures, 1 table
