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.
Competitive Advantages:
- An alternative to quantum computing
- Solves problems in a single cycle
- Solutions can be read-out in parallel
- Applicable to a wide range of non-convex quadratic problems
The Stages Involved in Object Recognition for the Current Technology
Desired Partnerships:
- License
- Sponsored Research
- Co-development