  The broad applicability of Quadratic Unconstrained Binary Optimization (QUBO)
constitutes a general-purpose modeling framework for combinatorial optimization
problems and are a required format for gate array and quantum annealing
computers. QUBO annealers as well as other solution approaches benefit from
starting with a diverse set of solutions with local optimality an additional
benefit. This paper presents a new method for generating a set of one-flip
local optima leveraging constraint programming. Further, as demonstrated in
experimental testing, analysis of the solution set allows the generation of
soft constraints to help guide the optimization process.
