Goal Seeking Quadratic Unconstrained Binary Optimization
This addresses the need for diverse, acceptable solutions in high-level decision-making for QUBO problems, which are crucial for quantum and digital annealers, but it is incremental as it builds on existing QUBO frameworks.
The paper tackles the difficulty of achieving optimality in Quadratic Unconstrained Binary Optimization (QUBO) problems by proposing two alternatives for goal-seeking QUBO that minimize deviation from a target or range, with experimental results showing efficacy over Constraint Programming for quickly finding a satisficing set of solutions.
The Quadratic Unconstrained Binary Optimization (QUBO) modeling and solution framework is a requirement for quantum and digital annealers. However optimality for QUBO problems of any practical size is extremely difficult to achieve. In order to incorporate the problem-specific insights, a diverse set of solutions meeting an acceptable target metric or goal is the preference in high level decision making. In this paper, we present two alternatives for goal-seeking QUBO for minimizing the deviation from a given target as well as a range of values around a target. Experimental results illustrate the efficacy of the proposed approach over Constraint Programming for quickly finding a satisficing set of solutions.