2018
Cluster-update recommenders in Monte Carlo simulations
Nobuyuki Yoshioka
Machine Learning with Tensor Networks
Eliska Greplova
Teaching machines to spot the essential
Two physicists at ETH Zurich and the Hebrew University of Jerusalem have developed a novel machine-learning algorithm that analyses large data sets describing a physical system and extract from them the essential information needed to understand the underlying physics.
Machine learning topological phases
Valerio Peri
Exact Wavefunctions with Deep Neural Networks
Frank Schindler
Reinforcement learning of the many-body wave function on RBMs
Luca Papariello
Supervised 'Machine Learning Phases of Matter'
Mark H Fischer
From PCA to Variational Autoencoders
Sebastian Huber
Machine learning in condensed matter physics
Maciej Koch-Janusz
Quantum physics turned into tangible reality
ETH physicists have developed a silicon wafer that behaves like a topological insulator when stimulated using ultrasound. They have thereby succeeded in turning an abstract theoretical concept into a macroscopic product.