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physics.comp-ph2020★ 30 cited
Controlled Online Optimization Learning (COOL): Finding the ground state of spin Hamiltonians with reinforcement learning
Kyle Mills, Pooya Ronagh, Isaac Tamblyn
Reinforcement learning (RL) has become a proven method for optimizing a procedure for which success has been defined, but the specific actions needed to achieve it have not. We app…
physics.comp-ph2019★ 22 cited
Effects of setting the temperatures in the parallel tempering Monte Carlo algorithm
Ignacio Rozada, Maliheh Aramon, Jonathan Machta +1
Parallel tempering Monte Carlo has proven to be an efficient method in optimization and sampling applications. Having an optimized temperature set enhances the efficiency of the al…
physics.comp-ph2018★ 451 cited
Physics-Inspired Optimization for Quadratic Unconstrained Problems Using a Digital Annealer
Maliheh Aramon, Gili Rosenberg, Elisabetta Valiante +3
The Fujitsu Digital Annealer (DA) is designed to solve fully connected quadratic unconstrained binary optimization (QUBO) problems. It is implemented on application-specific CMOS h…