Identifying quantum phase transitions using artificial neural networks on experimental data

A high-bias, low-variance introduction to Machine Learning for physicists

(2019) Pankaj Mehta et al.   PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS

Measuring quantized circular dichroism in ultracold topological matter

(2019) Luca Asteria et al.   Nature Physics

Measuring topology from dynamics by obtaining the Chern number from a linking number

(2019) Matthias Tarnowski et al.   Nature Communications

Machine learning in electronic-quantum-matter imaging experiments

(2019) Yi Zhang et al.   NATURE

Methods for interpreting and understanding deep neural networks

(2018) Grégoire Montavon et al.   DIGITAL SIGNAL PROCESSING

Neural-network quantum state tomography

(2018) Giacomo Torlai et al.   Nature Physics

Machine Learning Topological Invariants with Neural Networks

(2018) Pengfei Zhang et al.   PHYSICAL REVIEW LETTERS

Why Does Deep and Cheap Learning Work So Well?

(2017) Henry W. Lin et al.   JOURNAL OF STATISTICAL PHYSICS

Quantum machine learning

(2017) Jacob Biamonte et al.   NATURE

Learning phase transitions by confusion

(2017) Evert P. L. van Nieuwenburg et al.   Nature Physics

Machine learning phases of matter

(2017) Juan Carrasquilla et al.   Nature Physics

Mean-Field Scaling of the Superfluid to Mott Insulator Transition in a 2D Optical Superlattice

(2017) Claire K. Thomas et al.   PHYSICAL REVIEW LETTERS

Quantum Loop Topography for Machine Learning

(2017) Yi Zhang et al.   PHYSICAL REVIEW LETTERS

Colloquium: Atomic quantum gases in periodically driven optical lattices

(2017) André Eckardt   REVIEWS OF MODERN PHYSICS

Solving the quantum many-body problem with artificial neural networks

(2017) Giuseppe Carleo et al.   SCIENCE

Efficient representation of quantum many-body states with deep neural networks

(2017) Xun Gao et al.   Nature Communications

Machine learning quantum phases of matter beyond the fermion sign problem

(2017) Peter Broecker et al.   Scientific Reports

Deep Learning the Quantum Phase Transitions in Random Two-Dimensional Electron Systems

(2016) Tomoki Ohtsuki et al.   JOURNAL OF THE PHYSICAL SOCIETY OF JAPAN

Experimental reconstruction of the Berry curvature in a Floquet Bloch band

(2016) N. Flaschner et al.   SCIENCE

Fast machine-learning online optimization of ultra-cold-atom experiments

(2016) P. B. Wigley et al.   Scientific Reports

Deep learning

(2015) Yann LeCun et al.   NATURE

Experimental realization of the topological Haldane model with ultracold fermions

(2014) Gregor Jotzu et al.   NATURE

Measuring the Chern number of Hofstadter bands with ultracold bosonic atoms

(2014) M. Aidelsburger et al.   Nature Physics

Tomography of Band Insulators from Quench Dynamics

(2014) Philipp Hauke et al.   PHYSICAL REVIEW LETTERS

Quantum phase transitions of ultracold Bose systems in nonrectangular optical lattices

(2012) Zhi Lin et al.   PHYSICAL REVIEW A

Quantum Simulation of Frustrated Classical Magnetism in Triangular Optical Lattices

(2011) J. Struck et al.   SCIENCE

Reference data for phase diagrams of triangular and hexagonal bosonic lattices

(2010) N. Teichmann et al.   EPL

Single-atom-resolved fluorescence imaging of an atomic Mott insulator

(2010) Jacob F. Sherson et al.   NATURE

Ultracold quantum gases in triangular optical lattices

(2010) C Becker et al.   NEW JOURNAL OF PHYSICS

Probing the Superfluid-to-Mott Insulator Transition at the Single-Atom Level

(2010) W. S. Bakr et al.   SCIENCE

In situ observation of incompressible Mott-insulating domains in ultracold atomic gases

(2009) Nathan Gemelke et al.   NATURE

Photovoltaic Hall effect in graphene

(2009) Takashi Oka et al.   PHYSICAL REVIEW B

Many-body physics with ultracold gases

(2008) Immanuel Bloch et al.   REVIEWS OF MODERN PHYSICS