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This research investigates the relationship between subsystem quantum complexity and the emergence of computationally useful structures in postvariational quantum circuits. The study identifies an intermediate "learning phase" preceding quantum chaos, where spectral nonflatness and metrological response serve as critical resources for scalable nonlinear computation. These findings clarify how random quantum dynamics can be harnessed to enhance information processing capacity before deep scrambling suppresses observable responses.
The ebbs and flows of quantum learning and sensing
arXiv:2608.20155v1 Announce Type: new Abstract: 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.
Abstract: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.
Quantum Physics (quant-ph); Disordered Systems and Neural Networks (cond-mat.dis-nn); Statistical Mechanics (cond-mat.stat-mech); Strongly Correlated Electrons (cond-mat.str-el)
arXiv:2608.20155 [quant-ph]
(or arXiv:2608.20155v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2608.20155 arXiv-issued DOI via DataCite (pending registration)
From: Moein N. Ivaki [view email] [v1] Thu, 20 Aug 2026 15:14:28 UTC (9,077 KB)
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