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Investigation of Commuting Hamiltonian in Quantum Markov Network

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An Erratum to this article was published on 30 September 2014

Abstract

Graphical Models have various applications in science and engineering which include physics, bioinformatics, telecommunication and etc. Usage of graphical models needs complex computations in order to evaluation of marginal functions, so there are some powerful methods including mean field approximation, belief propagation algorithm and etc. Quantum graphical models have been recently developed in context of quantum information and computation, and quantum statistical physics, which is possible by generalization of classical probability theory to quantum theory. The main goal of this paper is preparing a primary generalization of Markov network, as a type of graphical models, to quantum case and applying in quantum statistical physics. We have investigated the Markov network and the role of commuting Hamiltonian terms in conditional independence with simple examples of quantum statistical physics.

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Authors and Affiliations

  1. Department of Physics, University of Tabriz, Tabriz, Iran

    Farzad Ghafari Jouneghani, Mohammad Babazadeh & Rogayeh Bayramzadeh

  2. Azar Aytash Co., Technology Incubator, University of Tabriz, Tabriz, Iran

    Hossein Movla

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  1. Farzad Ghafari Jouneghani
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  2. Mohammad Babazadeh
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  3. Rogayeh Bayramzadeh
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  4. Hossein Movla
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Correspondence to Farzad Ghafari Jouneghani.

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Jouneghani, F.G., Babazadeh, M., Bayramzadeh, R. et al. Investigation of Commuting Hamiltonian in Quantum Markov Network. Int J Theor Phys 53, 2521–2530 (2014). https://doi.org/10.1007/s10773-014-2042-8

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  • DOI: https://doi.org/10.1007/s10773-014-2042-8

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