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Tripartite mutual information, entanglement, and scrambling in permutation symmetric systems with an application to quantum chaos

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abstract

Many-body states that are invariant under particle relabelling, the permutation symmetric states, occur naturally when the system dynamics is described by symmetric processes or collective spin operators. We derive expressions for the reduced density matrix for arbitrary subsystem decomposition for these states and study properties of permutation symmetric states and their subsystems when the joint system is picked randomly and uniformly. Thus defining a new random matrix ensemble, we find the average linear entropy and von Neumann entropy which implies that random permutation symmetric states are marginally entangled and as a consequence the tripartite mutual information (TMI) is typically positive, preventing information from being shared globally. Applying these results to the quantum kicked top viewed as a multi-qubit system we find that entanglement, mutual information and TMI all increase for large subsystems across the Ehrenfest or log-time and saturate at the random state values if there is global chaos. During this time the out-of-time order correlators (OTOC) evolve exponentially implying scrambling in phase space. We discuss how positive TMI may coexist with such scrambling.

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hep-ph 1

years

2026 1

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CONDITIONAL 1

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unclear 1

representative citing papers

Improved Approximations for Collective Neutrino Oscillations

hep-ph · 2026-07-30 · conditional · novelty 5.0

A second-order BBGKY closure reproduces exact small-system neutrino dynamics about two orders of magnitude better than mean-field at polynomial cost, and predicts correlated large-N behavior.

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Showing 1 of 1 citing paper.

  • Improved Approximations for Collective Neutrino Oscillations hep-ph · 2026-07-30 · conditional · none · ref 47 · internal anchor

    A second-order BBGKY closure reproduces exact small-system neutrino dynamics about two orders of magnitude better than mean-field at polynomial cost, and predicts correlated large-N behavior.