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Operational Metric for Quantum Chaos and the Corresponding Spatiotemporal Entanglement Structure

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arxiv 2210.14926 v4 pith:HP5G4WQC submitted 2022-10-26 quant-ph cond-mat.stat-mech

classification quant-phcond-mat.stat-mech
keywords quantumchaosentanglementmany-bodyoperationalsciencessensitivediagnostics
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Chaotic systems are highly sensitive to a small perturbation, and are ubiquitous throughout biological sciences, physical sciences and even social sciences. Taking this as the underlying principle, we construct an operational notion for quantum chaos. Namely, we demand that the future state of a many-body, isolated quantum system is sensitive to past multitime operations on a small subpart of that system. By `sensitive', we mean that the resultant states from two different perturbations cannot easily be transformed into each other. That is, the pertinent quantity is the complexity of the effect of the perturbation within the final state. From this intuitive metric, which we call the Butterfly Flutter Fidelity, we use the language of multitime quantum processes to identify a series of operational conditions on chaos, in particular the scaling of the spatiotemporal entanglement. Our criteria already contain the routine notions, as well as the well-known diagnostics for quantum chaos. This includes the Peres-Loschmidt Echo, Dynamical Entropy, Tripartite Mutual Information, and Local-Operator Entanglement. We hence present a unified framework for these existing diagnostics within a single structure. We also go on to quantify how several mechanisms lead to quantum chaos, such as evolution generated from random circuits. Our work paves the way to systematically study many-body dynamical phenomena like Many-Body Localization, measurement-induced phase transitions, and Floquet dynamics.

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Cited by 1 Pith paper

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  1. Thermalization with partial information

    quant-ph 2025-08 unverdicted novelty 6.0 of 10

    A maximum channel entropy principle, backed by a microcanonical-style derivation, identifies the canonical noisy channel that models thermalization under partial information.

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