1.5k citations · 11.2k across the 149 of their papers we have counts for
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Dynamic scaling of topological ordering in classical systems
Na Xu, Claudio Castelnovo, Roger G. Melko +2
We analyze scaling behaviors of simulated annealing carried out on various classical systems with topological order, obtained as appropriate limits of the toric code in two and thr…
Machine learning vortices at the Kosterlitz-Thouless transition
Matthew J. S. Beach, Anna Golubeva, Roger G. Melko
Efficient and automated classification of phases from minimally processed data is one goal of machine learning in condensed matter and statistical physics. Supervised algorithms tr…
Deep Learning the Ising Model Near Criticality
Alan Morningstar, Roger G. Melko
It is well established that neural networks with deep architectures perform better than shallow networks for many tasks in machine learning. In statistical physics, while there has…
Machine Learning Quantum Spin Liquids with Quasi-particle Statistics
Yi Zhang, Roger G. Melko, Eun-Ah Kim
After decades of progress and effort, obtaining a phase diagram for a strongly-correlated topological system still remains a challenge. Although in principle one could turn to Wils…
Kernel methods for interpretable machine learning of order parameters
Pedro Ponte, Roger G. Melko
Machine learning is capable of discriminating phases of matter, and finding associated phase transitions, directly from large data sets of raw state configurations. In the context…
Tripartite entangled plaquette state in a cluster magnet
Juan Carrasquilla, Gang Chen, Roger G. Melko
Using large-scale quantum Monte Carlo simulations we show that a spin- XXZ model on a two-dimensional anisotropic Kagome lattice exhibits a tripartite entangled plaquette stat…