# Recoverability from direct quantum correlations

## TL;DR

This paper develops an efficient method for learning and compressing quantum states based on partial information, focusing on tree-structured bipartite marginals and quantum Markov chains, with implications for quantum information processing.

## Contribution

It introduces a new algebraic condition for quantum Markov chain compatibility and extends the approach to n-partite systems, improving quantum state reconstruction methods.

## Key findings

- Provided a necessary and sufficient algebraic condition for quantum Markov chain compatibility.
- Demonstrated efficient selection of bipartite marginals under the pairwise Markov condition.
- Extended the framework to n-partite quantum systems with preliminary results.

## Abstract

We address the problem of compressing density operators defined on a finite dimensional Hilbert space which assumes a tensor product decomposition. In particular, we look for an efficient procedure for learning the most likely density operator, according to Jaynes' principle, given a chosen set of partial information obtained from the unknown quantum system we wish to describe. For complexity reasons, we restrict our analysis to tree-structured sets of bipartite marginals. We focus on the tripartite scenario, where we solve the problem for the couples of measured marginals which are compatible with a quantum Markov chain, providing then an algebraic necessary and sufficient condition for the compatibility to be verified. We introduce the generalization of the procedure to the n-partite scenario, giving some preliminary results. In particular, we prove that if the pairwise Markov condition holds between the subparts then the choice of the best set of tree-structured bipartite marginals can be performed efficiently. Moreover, we provide a new characterisation of quantum Markov chains in terms of quantum Bayesian updating processes.