Constraint Programming to Discover One-Flip Local Optima of Quadratic Unconstrained Binary Optimization Problems

• URL: http://arxiv.org/abs/2104.01709v1
• Date: Sun, 4 Apr 2021 22:55:25 GMT
• Title: Constraint Programming to Discover One-Flip Local Optima of Quadratic Unconstrained Binary Optimization Problems
• Authors: Amit Verma and Mark Lewis
• Abstract summary: QUBO annealers as well as other solution approaches benefit from starting with a diverse set of solutions with local optimality. This paper presents a new method for generating a set of one-flip local optima leveraging constraint programming.
• Score: 0.5439020425819
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The broad applicability of Quadratic Unconstrained Binary Optimization (QUBO) constitutes a general-purpose modeling framework for combinatorial optimization problems and are a required format for gate array and quantum annealing computers. QUBO annealers as well as other solution approaches benefit from starting with a diverse set of solutions with local optimality an additional benefit. This paper presents a new method for generating a set of one-flip local optima leveraging constraint programming. Further, as demonstrated in experimental testing, analysis of the solution set allows the generation of soft constraints to help guide the optimization process.

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