Quantum optimization algorithms are quantum algorithms that are used to solve optimization problems. [1] Mathematical optimization deals with finding the best solution to a problem (according to some criteria) from a set of possible solutions. Mostly, the optimization problem is formulated as a minimization problem, where one tries to minimize.. Solving real-world optimization problems with quantum computing requires choosing between a large number of options concerning formulation, encoding, algorithm and hardware. Finding good solution paths is challenging for end users and researchers alike. We propose a framework designed to identify and recommend the best-suited solution paths. This introduces a novel abstraction layer that is.
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Optimization by Quantum Computing (QC) includes, on the one hand, quantum annealers 13, designed to solve specific optimization problems by mapping them onto physical quantum Hamiltonian and have.. Quantum computers come in a number of forms, but the most useful to O.R. and analytics professionals is known as a "quantum annealer.". Operators of these systems convert real-world problems into a polynomial unconstrained binary optimization (PUBO) expression of the sort that should be familiar to anyone who has tackled the traveling.
