USF researchers have developed a system and method that harnesses the innate physical properties of nano-magnets to directly solve computationally hard problems in a one-shot fashion. This approach can be applied to a wide range of non-convex quadratic optimization problems. Specifically, this physics-inspired computing methodology maps quadratic energy minimization problem spaces into a set of interacting magnets. Optimization is accomplished by the relaxation physics of the magnets themselves, and solutions can be read-out in parallel. The present invention provides a system and method for directly solving computationally difficult problems with a single input-output cycle in less time than traditional methods.
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