Universal recovery map for approximate Markov chains

TL;DR

This paper demonstrates that the quantum conditional mutual information quantifies the effectiveness of recovery operations in quantum states, establishing a universal bound for approximate Markov chains with implications for topological order.

Contribution

It proves that the quantum conditional mutual information bounds the distance to the best recovery map, providing a universal recovery map for approximate quantum Markov chains.

Findings

Conditional mutual information bounds recovery performance

Recovery map depends only on the reduced state $ ho_{BC}$

Implications for defining topological order in quantum systems

Abstract

A central question in quantum information theory is to determine how well lost information can be reconstructed. Crucially, the corresponding recovery operation should perform well without knowing the information to be reconstructed. In this work, we show that the quantum conditional mutual information measures the performance of such recovery operations. More precisely, we prove that the conditional mutual information $I(A:C|B)$ of a tripartite quantum state $\rho_{ABC}$ can be bounded from below by its distance to the closest recovered state $\mathcal{R}_{B \to BC}(\rho_{AB})$, where the $C$-part is reconstructed from the $B$-part only and the recovery map $\mathcal{R}_{B \to BC}$ merely depends on $\rho_{BC}$. One particular application of this result implies the equivalence between two different approaches to define topological order in quantum systems.