Sampling combinatorial energy landscapes by classical and quantum computation
Combinatorial and, by extension, non-convex optimization problems are among the most difficult to solve computationally due to the inadequacy of local search methods alone to find global optima. The notion of an energy landscape provides a unified language to describe the origin of combinatorial complexity across a wide variety of fields including physics, materials science, and artificial intelligence. This thesis utilizes probabilistic techniques for efficiently sampling energy landscapes that rely on classical high-performance computers and near-term quantum computers. Such sampling techniques ...
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