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Reflected entropy is not a correlation measure

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arxiv 2302.10208 v2 pith:SKNCZPOK submitted 2023-02-20 hep-th quant-ph

classification hep-thquant-ph
keywords reflectedentropymeasurealphaclassicalcorrelationcorrelationscounterexamples
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

By explicit counterexample, we show that the "reflected entropy" defined by Dutta and Faulkner is not monotonically decreasing under partial trace, and so is not a measure of physical correlations. In fact, our counterexamples show that none of the R\'enyi reflected entropies $S_{R}^{(\alpha)}$ for $0 < \alpha < 2$ is a correlation measure; the usual reflected entropy is realized as the $\alpha=1$ member of this family. The counterexamples are given by quantum states that correspond to classical probability distributions, so reflected entropy fails to measure correlations even at the classical level.

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Cited by 3 Pith papers

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

  1. Surviving correlations across a horizon: reflected entropy for bosonic fields in non-inertial frames and black hole spacetimes

    hep-th 2026-08 conditional novelty 6.0 of 10

    Bosonic Alice-Bob reflected entropy saturates at nonzero floors (1.757 bits for Bell, 0.315 for GHZ) at infinite acceleration, while the inter-wedge reflected entropy diverges linearly in the squeezing parameter.

  2. The Entanglement Wedge Polygon

    hep-th 2026-06 unverdicted novelty 6.0 of 10

    The paper defines the entanglement wedge polygon as the intersection of entanglement wedges external to individual homology regions and studies its topological and geometric properties in AdS examples.

  3. Additivity of disjoint interval entanglement in quasiparticle excited states

    quant-ph 2026-01 conditional novelty 6.0 of 10

    For quasiparticle excited states with large momentum differences, double-interval reflected entropy, mutual information, and logarithmic negativity add: X_{K1∪K2} = X_{K1} + X_{K2}.

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