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Necessary Criterion for Approximate Recoverability

David Sutter, Renato Renner

Published Oct 1, 2018• 10 citations
Publisher

Abstract

A tripartite state ρ_ABC forms a Markov chain if there exists a recovery map R_B→_BC acting only on the B-part that perfectly reconstructs ρ_ABC from ρ_AB. To achieve an approximate reconstruction, it suffices that the conditional mutual information I(A:C|B)_ρ is small, as shown recently. Here we ask what conditions are necessary for approximate state reconstruction. This is answered by a lower bound on the relative entropy between ρ_ABC and the recovered state R_B→_BC(ρ_AB). The bound consists of the conditional mutual information and an entropic correction term that quantifies the disturbance of the B-part by the recovery map.

DOI
10.3929/ethz-b-000289928

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A tripartite state ρ_ABC forms a Markov chain if there exists a recovery map R_B→_BC acting only on the B-part that perfectly reconstructs ρ_ABC from ρ_AB. To achieve an approximate reconstruction, it suffices that the conditional mutual information I(A:C|B)_ρ is small, as shown recently. Here we ask what conditions are necessary for approximate state reconstruction. This is answered by a lower bound on the relative entropy between ρ_ABC and the recovered state R_B→_BC(ρ_AB). The bound consists of the conditional mutual information and an entropic correction term that quantifies the disturbance of the B-part by the recovery map.
A tripartite state ρABC\rho _{ABC}ρABC​ forms a Markov chain if there exists a recovery map RB→BC\mathcal {R}_{B \rightarrow BC}RB→BC​ acting only on the B-part that perfectly reconstructs ρABC\rho _{ABC}ρABC​ from ρAB\rho _{AB}ρAB​ . To achieve an approximate reconstruction, it suffices that the conditional mutual information I(A:C∣B)ρI(A:C|B)_{\rho }I(A:C∣B)ρ​ is small, as shown recently. Here we ask what conditions are necessary for approximate state reconstruction. This is answered by a lower bound on the relative entropy between ρABC\rho _{ABC}ρABC​ and the recovered state RB→BC(ρAB)\mathcal {R}_{B\rightarrow BC}(\rho _{AB})RB→BC​(ρAB​) . The bound consists of the conditional mutual information and an entropic correction term that quantifies the disturbance of the B-part by the recovery map.
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 ρ ABC can be bounded from below by its distance to the closest recovered state R B → B C ( ρ A B ) , where the C -part is reconstructed from the B -part only and the recovery map R B → B C merely depends on ρ BC . One particular application of this result implies the equivalence between two different approaches to define topological order in quantum systems.
A Markov chain is a tripartite quantum state ρABCwhere there exists a recovery map RB→BCsuch that ρABC= RB→BC(ρAB). More generally, an approximate Markov chain ρABCis a state whose distance to the closest recovered state RB→BC(ρAB) is small. Recently it has been shown that this distance can be bounded from above by the conditional mutual information I(A : C|B)ρof the state. We improve on this connection by deriving the first bound that is tight in the commutative case and features an explicit recovery map that only depends on the reduced state pBC. The key tool in our proof is a multivariate extension of the Golden-Thompson inequality, which allows us to extend logarithmic trace inequalities from two to arbitrarily many matrices.
A state on a tripartite quantum system A⊗B⊗C{A \otimes B \otimes C}A⊗B⊗C forms a Markov chain if it can be reconstructed from its marginal on A⊗B{A \otimes B}A⊗B by a quantum operation from B to B⊗C{B \otimes C}B⊗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.
A short quantum Markov chain is a tripartite state such that system A can be recovered perfectly by acting on system C of the reduced state Such states have conditional mutual information equal to zero and are the only states with this property. A quantum channel is sufficient for two states ρ and σ if there exists a recovery channel using which one can perfectly recover ρ from and σ from The relative entropy difference is equal to zero if and only if is sufficient for ρ and σ . In this paper, we show that these properties extend to Rényi generalizations of these information measures which were proposed in (Berta et al 2015 J. Math. Phys. 56 022205 ; Seshadreesan et al 2015 J. Phys. A: Math. Theor. 48 395303 ), thus providing an alternate characterization of short quantum Markov chains and sufficient quantum channels. These results give further support to these quantities as being legitimate Rényi generalizations of the conditional mutual information and the relative entropy difference. Along the way, we solve some open questions of Ruskai and Zhang, regarding the trace of particular matrices that arise in the study of monotonicity of relative entropy under quantum operations and strong subadditivity of the von Neumann entropy.
Quantum many-body Gibbs states satisfy an approximate local Markov property~\cite{chen2025GibbsMarkov}: local noise can be approximately recovered by a quasi-local recovery map, and the conditional mutual information decays for the corresponding tripartition. Recent work~\cite{bergamaschi2025structural} extends this property to approximate stationary states (metastable states) of certain master equations modeling system--bath dynamics, and proposes a strengthened post-selected recovery property requiring recovery to hold for each measurement outcome. In this note, we characterize this \textit{strong Markov property}: it holds if and only if the state additionally satisfies correlation decay for suitable pairs of observables. We further prove several structural and operational consequences of the strong Markov property in the presence of an underlying master equation. First, one can estimate multiple observables from a \textit{single copy} of the state via a repeated measurement--recovery protocol. Second, any two strongly Markov states must have local marginals that are either very close or well separated. Third, if a strongly Markov state can be expressed as a mixture of two strongly Markov states, then their local marginals must be nearly indistinguishable.