AI 中文总结
该研究探究子系统量子复杂性与计算有用结构涌现的关联，通过后变分量子电路揭示量子混沌前的“学习相”，发现最优信息处理容量随系统尺寸增大而提升，相关特征可作为可扩展非线性计算资源。
AI 中文摘要
子系统量子复杂性与计算有用结构的涌现之间存在何种关联？我们通过研究一类微调程度最小的后变分量子电路来解决这一问题，结果表明谱非平坦性与计量响应直接控制系综典型的信息处理能力。这揭示了量子混沌 onset 之前存在一个中间“学习相”，其特征为显著的非平坦性和读出态的敏感性。最优信息处理容量随系统尺寸增大而提升，而深度 scrambling（量子 scrambling，量子 scrambling 是量子信息中描述量子态混乱程度的专有术语）会抑制可观测响应。这些结果表明，随机量子动力学的此类特征可被视为可扩展非线性计算的计算资源。
英文摘要
What is the relation between subsystem quantum complexity and the emergence of computationally useful structure? We address this by studying a family of minimally tunable postvariational quantum circuits, and show how spectral nonflatness and metrological response directly control the ensemble-typical information processing power. This unveils an intermediate "learning phase" that precedes the onset of quantum chaos, characterized by pronounced nonflatness and sensitivity of readout states. The optimal information processing capacity improves with system size, while deep scrambling suppresses observable response. The results reveal how such features of random quantum dynamics can be viewed as computational resources for scalable nonlinear computation.
