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Efficient QUBO transformation for Higher Degree Pseudo Boolean Functions

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arxiv 2107.11695 v1 pith:ALHCJT2L submitted 2021-07-24 math.OC cs.AI

classification math.OCcs.AI
keywords qubodegreehigherproblemstransformationvariablesadditionalapproach
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Quadratic Unconstrained Binary Optimization (QUBO) is recognized as a unifying framework for modeling a wide range of problems. Problems can be solved with commercial solvers customized for solving QUBO and since QUBO have degree two, it is useful to have a method for transforming higher degree pseudo-Boolean problems to QUBO format. The standard transformation approach requires additional auxiliary variables supported by penalty terms for each higher degree term. This paper improves on the existing cubic-to-quadratic transformation approach by minimizing the number of additional variables as well as penalty coefficient. Extensive experimental testing on Max 3-SAT modeled as QUBO shows a near 100% reduction in the subproblem size used for minimization of the number of auxiliary variables.

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  1. Boosting quantum annealing performance through direct polynomial unconstrained binary optimization

    quant-ph 2024-12 conditional novelty 6.0 of 10

    For 3-SAT problems, direct PUBO encoding shows larger minimum energy gaps than a standard QUBO reduction, hinting at an exponential speedup for some problem families.

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