175 citations · 362 across the 9 of their papers we have counts for
3 papers · 1 filter
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…
Optical lattice experiments at unobserved conditions and scales through generative adversarial deep learning
Corneel Casert, Kyle Mills, Tom Vieijra +2
Machine learning provides a novel avenue for the study of experimental realizations of many-body systems, and has recently been proven successful in analyzing properties of experim…
Extensive deep neural networks for transferring small scale learning to large scale systems
Kyle Mills, Kevin Ryczko, Iryna Luchak +3
We present a physically-motivated topology of a deep neural network that can efficiently infer extensive parameters (such as energy, entropy, or number of particles) of arbitrarily…