# Goal Seeking Quadratic Unconstrained Binary Optimization

arXiv: https://arxiv.org/abs/2103.12951 · HTML: https://mlsanity.com/paper/2103.12951/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 25 references that are in the map.
- First posted on arXiv 24 Mar 2021.

## Built on by (most cited first, 1 of 1)

- [Efficient QUBO transformation for Higher Degree Pseudo Boolean Functions](https://mlsanity.com/paper/2107.11695/) (2021)

## Abstract (arXiv)

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.