发表机构
Centre for Artificial Intelligence, Easwari Engineering College; Center for Cognitive Science, Trichy SRM Medical College Hospital and Research Center; Centre for Computational Biology, Easwari Engineering College; Department of Physics, Central University of Rajasthan; Department of Physics and Astrophysics, University of Delhi（Easwari工程学院人工智能中心; 特吕希SRM医学院医院与研究中心认知科学中心; Easwari工程学院计算生物学中心; 拉贾斯坦中央大学物理系; 德里大学物理与天体物理学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用AAH量子储层，通过自由费米子跃迁稀疏性实现奇异非混沌吸引子与混沌的高精度分类，并验证其可扩展性与通用性，同时应用于天文光变曲线筛选。
AI 中文摘要
Aubry-André-Harper（AAH）量子储层对奇异非混沌吸引子（SNAs）与混沌进行分类，其性能显著优于基于全Fock空间上的稠密随机矩阵构建的储层。消融实验和受控干预表明，在我们的控制条件下，主导因素是自由费米子单体跃迁稀疏性，而非接近量子相变，因为$H_0$在费米子算符中是二次的，且在$65{,}280$个Fock空间跃迁中仅有$N(N-1)2^{N-2}=3584$个承载权重。保留这种二次结构的随机在位无序与AAH精度相匹配，而破坏它的全Hilbert空间随机矩阵和本征矢量置换则退化至随机基线水平。我们用哈密顿量跃迁核刻画由此产生的频率选择性滤波，将其验证至$N=8$量子比特，并在第二个动力系统中复现。我们还将该储层用作无训练筛选器，用于开普勒RR天琴座光变曲线中的奇异非混沌结构检测。
英文摘要
An Aubry-André-Harper (AAH) quantum reservoir classifies strange nonchaotic attractors (SNAs) against chaos, substantially outperforming reservoirs built from dense random matrices on the full Fock space. Ablations and controlled interventions identify \emph{free-fermion one-body transition sparsity}, rather than proximity to a quantum phase transition, as the dominant factor under our controls, since $H_0$ is quadratic in fermion operators and only $N(N-1)2^{N-2}=3584$ of the $65{,}280$ Fock-space transitions carry weight. Random on-site disorder, which preserves this quadratic structure, matches AAH accuracy, whereas full-Hilbert-space random matrices and eigenvector permutations, which break it, collapse to the random baseline. We characterize the resulting frequency-selective filtering with a Hamiltonian transition kernel, validate it to $N=8$ qubits, and reproduce it in a second dynamical system. We also use the reservoir as a training-free screen for strange-nonchaotic structure in Kepler RR Lyrae light curves.
Comments26 pages, 3 figures
