Goal Seeking Quadratic Unconstrained Binary Optimization

TL;DR

This paper introduces goal-seeking methods for QUBO problems that efficiently find solutions close to a target, improving over traditional constraint programming in speed and solution quality.

Contribution

It proposes two novel goal-seeking QUBO formulations for minimizing deviation from targets, enhancing solution efficiency and flexibility.

Findings

Proposed methods effectively find solutions near target values.

Experimental results outperform constraint programming in speed.

Approach is applicable to various goal ranges.

Abstract

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.