Belief Propagation algorithms acting on Graphical Models of classical probability distributions, such as Markov Networks, Factor Graphs and Bayesian Networks, are amongst the most powerful known methods for deriving probabilistic inferences amongst large numbers of random variables. This paper presents a generalization of these concepts and methods to the quantum case, based on the idea that quantum theory can be thought of as a noncommutative, operator-valued, generalization of classical probability theory. Some novel characterizations of quantum conditional independence are derived, and definitions of Quantum n-Bifactor Networks, Markov Networks, Factor Graphs and Bayesian Networks are proposed. The structure of Quantum Markov Networks is investigated and some partial characterization results are obtained, along the lines of the Hammersely-Clifford theorem. A Quantum Belief Propagation algorithm is presented and is shown to converge on 1-Bifactor Networks and Markov Networks when the underlying graph is a tree. The use of Quantum Belief Propagation as a heuristic algorithm in cases where it is not known to converge is discussed. Applications to decoding quantum error correcting codes and to the simulation of many-body quantum systems are described.
There has also been work on Quantum Markov networks within the quantum probability literature @cite_35 @cite_5 @cite_52 , although Belief Propagation has not been investigated in this literature. This is closer to the spirit of the present work, in the sense that it is based on the generalization of classical probability to a noncommutative, operator-valued probability theory. These works are primarily concerned with defining the Markov condition in such a way that it can be applied to systems with an infinite number of degrees of freedom, and hence an operator algebraic formalism is used. This is important for applications to statistical physics because the thermodynamic limit can be formally defined as the limit of an infinite number of systems, but it is not so important for numerical simulations, since these necessarily operate with a finite number of discretized degrees of freedom. Also conditional independence is defined in a different way via quantum conditional expectations, rather than the approach based on conditional mutual information and conditional density operators used in the present work. Nevertheless, it seems likely that there are connections to our approach that should to be investigated in future work.
@cite_35 @cite_5 @cite_52
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Abstract The program relative to the investigation of quantum Markov states for general one-dimensional spin models is carried on, following a strategy developed in the last years. In such a way, the emerging structure is fully clarified. This analysis is a starting point for the solution of the basic (still open) problem concerning the construction of a satisfactory theory of quantum Markov fields, i.e. quantum Markov processes with multi-dimensional indices. We review recent developments in the theory of quantum Markov states on the standard @math --spin lattice. A Dobrushin theory for quantum Markov fields is proposed. In the one--di -men -sional case where the order plays a crucial role, the structure arising from a quantum Markov state is fully understood. In this situation we obtain a splitting of a Markov state into a classical part, and a purely quantum part. This result allows us to provide a reconstruction theorem for quantum Markov states on chains