Estimation of Quantum Umlaut Information

arXiv:2610.41282v1 Announce Type: cross Abstract: Quantifying correlations is important for understanding how quantum states and channels support information processing. Quantum umlaut information $U$ connects such correlations to reliability in hypothesis testing and certain communication settings, but its known formulas require classical descriptions of the underlying states. To estimate correlations without these descriptions, we introduce measured quantum umlaut information $U^M$ by replacing Umegaki relative entropy with measured relative entropy. Its variational formulation leads to a quantum generative adversarial framework for estimating $U^M$ from measurement statistics of unknown bipartite states and classical-quantum channels. This framework optimizes reference states and measurements without reconstructing the target states or channel outputs. For classical-quantum channels, it also optimizes the input probabilities. We further analyze the relation between $U^M$ and $U$, deriving a state-dependent gap bound and characterizing equality for full-rank bipartite states. Numerical simulations show trained estimates close on average to independently computed numerical references for $U^M$ across the tested states and channels. These estimates could help assess how well quantum memories preserve correlations and how noise or drift affects input-output dependence in quantum transmitters and state-preparation devices.

Abstract:Quantifying correlations is important for understanding how quantum states and channels support information processing. Quantum umlaut information $U$ connects such correlations to reliability in hypothesis testing and certain communication settings, but its known formulas require classical descriptions of the underlying states. To estimate correlations without these descriptions, we introduce measured quantum umlaut information $U^M$ by replacing Umegaki relative entropy with measured relative entropy. Its variational formulation leads to a quantum generative adversarial framework for estimating $U^M$ from measurement statistics of unknown bipartite states and classical-quantum channels. This framework optimizes reference states and measurements without reconstructing the target states or channel outputs. For classical-quantum channels, it also optimizes the input probabilities. We further analyze the relation between $U^M$ and $U$, deriving a state-dependent gap bound and characterizing equality for full-rank bipartite states. Numerical simulations show trained estimates close on average to independently computed numerical references for $U^M$ across the tested states and channels. These estimates could help assess how well quantum memories preserve correlations and how noise or drift affects input-output dependence in quantum transmitters and state-preparation devices.

Quantum Physics (quant-ph); Information Theory (cs.IT); Machine Learning (cs.LG)

arXiv:2610.41282 [quant-ph]

(or arXiv:2610.41282v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2610.41282 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Estimation of Quantum Umlaut Information

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