A mixed-integer linear programming (MILP) formulation is presented forparameter estimation of the Potts model . This is useful in the development of energy-based graph models to be simulated on Quantum annealing hardware where the exact simulationtemperature is unknown . Computationally, the memory requirement in this method grows exponentially with the graph size. Therefore, this method can only bepractically applied to small graphs. Learning large data sets poses no extra cost to this method; however, applications involving the learning of high dimensional data are out of scope. Such applications include learning of small generative classifiers and spin-lattice model with energy described byIsing hamiltonian . In both instances, the optimizationprocess ensures that the

Author(s) : Siddhartha Srivastava, Veera Sundararaghavan

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Keywords : learning - method - model - graphs - small -

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