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Disorder-free Sachdev-Ye-Kitaev models: Integrability and a precursor of chaos

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arxiv 2402.13154 v4 pith:XZ4FIN23 submitted 2024-02-20 cond-mat.str-el cond-mat.stat-mechhep-thquant-ph

classification cond-mat.str-elcond-mat.stat-mechhep-thquant-ph
keywords modelsbehaviordisorder-freeintegrabilitysystemschaoticdynamicalotocs
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abstract

We introduce two disorder-free variants of the Sachdev-Ye-Kitaev (SYK) model, demonstrate their integrability, and study their static and dynamical properties. Unlike diagrammatic techniques, the integrability of these models allows us to obtain dynamical correlation functions even when the number of Majorana fermions is finite. From the solutions, we find that out-of-time-order correlators (OTOCs) in these models exhibit exponential growth at early times, resembling that of quantum chaotic systems, such as those with disorder or external kick terms, despite their large $N$ behavior differing from that of typical chaotic systems. Conversely, our analysis shows no evidence of random-matrix behavior in level statistics or the spectral form factor. Our findings illustrate that the clean versions of the SYK models represent simple but nontrivial examples of disorder-free quantum many-body systems displaying chaos-like behavior of OTOCs.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Entanglement production in the Sachdev-Ye-Kitaev Model and its variants

    quant-ph 2025-07 conditional novelty 6.0 of 10

    Entanglement production rates distinguish the spin-SYK model from fermionic SYK and binary SYK, and the differences only become visible at larger system sizes.

  2. Chaotic-Integrable Transition for Disordered Orbital Hatsugai-Kohmoto Model

    cond-mat.str-el 2024-11 conditional novelty 5.0 of 10

    Disordered orbital Hatsugai-Kohmoto model shows a transition from Poisson to GOE level statistics as interaction disorder increases, while OTOC plateau values fail to uniformly distinguish chaos across models.

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