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.
翻译：量子和数字肛交器需要量子模型和解决方案框架(QUBO),但任何实际规模的QUBO问题都极难实现最佳性。为了纳入针对具体问题的洞察力,一套符合可接受的指标或目标的多样化解决方案是高层次决策的偏好。本文为寻求目标的QUBO提出了两个备选方案,以尽量减少偏离特定目标的情况以及围绕目标的一系列价值。实验结果表明,拟议采用的方法优于快速找到一套有讽刺意味的解决办法的节制方案编制方法,其效果显著。
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