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We propose a novel method for reducing the number of variables in quadratic unconstrained binary optimization problems, using a quantum annealer (or any sampler) to fix the value of a large portion of the variables to values that have a high probability of being optimal.
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Bunyk, P.I., Hoskinson, E.M., Johnson, M.W., Tolkacheva, E., Altomare, F., Berkley, A.J., Harris, R., Hilton, J.P., Lanting, T., Przybysz, A.J., et al.: Architectural considerations in the design of a superconducting quantum annealing processor · 2014
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Perdomo-Ortiz, A., O’Gorman, B., Fluegemann, J., Biswas, R., Smelyanskiy, V.N.: Determination and correction of persistent biases in quantum annealers · 2016
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Rosenberg, G., Haghnegahdar, P., Goddard, P., Carr, P., Wu, K., de Prado, M.L.: Solving the optimal trading trajectory problem using a quantum annealer · 2016
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