  The Quadratic Unconstrained Binary Optimization (QUBO) modeling and solution
framework is required for quantum and digital annealers whose goal is the
optimization of a well defined metric, the objective function. However, diverse
suboptimal solutions may be preferred over harder to implement strict optimal
ones. In addition, the decision-maker usually has insights that are not always
efficiently translated into the optimization model, such as acceptable target,
interval or range values. Multi-criteria decision making is an example of
involving the user in the decision process. In this paper, we present two
variants of goal-seeking QUBO that minimize the deviation from the goal through
a tabu-search based greedy one-flip heuristic. Experimental results illustrate
the efficacy of the proposed approach over Constraint Programming for quickly
finding a satisficing set of solutions.
