  Quadratic Unconstrained Binary Optimization models are useful for solving a
diverse range of optimization problems. Constraints can be added by
incorporating quadratic penalty terms into the objective, often with the
introduction of slack variables needed for conversion of inequalities. This
transformation can lead to a significant increase in the size and density of
the problem. Herein, we propose an efficient approach for recasting inequality
constraints that reduces the number of linear and quadratic variables.
Experimental results illustrate the efficacy.
