Constrained optimization problems have appeared in a wide variety of challenging real-world problems, where constraints often capture the physics underlying system. Classic methods for solving these relied on iterative algorithms that explored feasible domain search best solution. These became computational bottleneck decision-making and adversely impacted time-sensitive applications. Recently,...

The quantum approximate optimization algorithm (QAOA) by Farhi et al. is a computational framework for solving or classical tasks. Here, we explore using QAOA binary linear least squares (BLLS); problem that can serve as building block of several other hard problems in algebra, such the non-negative matrix factorization (NBMF) and variants (NMF) problem. Most previous efforts computing these we...