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Quantum conditional mutual information and approximate Markov chains

3 Pith papers cite this work. Polarity classification is still indexing.

3 Pith papers citing it
abstract

A state on a tripartite quantum system $A \otimes B \otimes C$ forms a Markov chain if it can be reconstructed from its marginal on $A \otimes B$ by a quantum operation from $B$ to $B \otimes C$. We show that the quantum conditional mutual information $I(A: C | B)$ of an arbitrary state is an upper bound on its distance to the closest reconstructed state. It thus quantifies how well the Markov chain property is approximated.

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Connecting Quantum Tomography and Quantum Retrodiction

quant-ph · 2026-06-22 · unverdicted · novelty 5.0

The Petz recovery map equals the gradient of the log-likelihood in maximum-likelihood tomography, unifying retrodiction and state reconstruction via a shared iterative procedure.

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Showing 2 of 2 citing papers after filters.

  • Conditional Independence of 1D Gibbs States with Applications to Efficient Learning quant-ph · 2024-02-28 · unverdicted · none · ref 34 · internal anchor

    1D translation-invariant Gibbs states at positive temperature exhibit superexponential decay of Belavkin-Staszewski conditional mutual information, enabling efficient learning from local measurements and tensor network approximations.

  • Connecting Quantum Tomography and Quantum Retrodiction quant-ph · 2026-06-22 · unverdicted · none · ref 43 · internal anchor

    The Petz recovery map equals the gradient of the log-likelihood in maximum-likelihood tomography, unifying retrodiction and state reconstruction via a shared iterative procedure.