  Quadratic Unconstrained Binary Optimization (QUBO) is a general-purpose
modeling framework for combinatorial optimization problems and is a requirement
for quantum annealers. This paper utilizes the eigenvalue decomposition of the
underlying Q matrix to alter and improve the search process by extracting the
information from dominant eigenvalues and eigenvectors to implicitly guide the
search towards promising areas of the solution landscape. Computational results
on benchmark datasets illustrate the efficacy of our routine demonstrating
significant performance improvements on problems with dominant eigenvalues.
