CONVENER

Victor Kagalovsky (Shamoon College of Engineering, Israel)

MEMBERS

Sergey Kravchenko(Northeastern university, USA)

Mikhail Fistul (Ruhr-University Bochum, Germany)

Tomi Ohtsuki (Sophia University, Japan)

Keith Slevin (Osaka University, Japan)

Igor Yurkevich (Aston University, UK)

Alexei Andreanov (PCS IBS)

OVERVIEW

Today machine learning is perfecting its abilities and becoming the most efficient and important tool in condensed matter physics. Obtaining and representing the ground and excited states’ wave functions are examples of such applications. Another application is analyzing the wave functions and determining their quantum phases.

The research of the Advanced Study Group (ASG) “Deep Learning in Quantum Phase Transitions” is planned to focus on two major applications: topological insulators and quantum strongly interacting many-body systems. Restricted Boltzmann Machines (RBM) deep learning method allows studying thermodynamics and spatial correlations of quantum phases in such systems.

Topics include:

- Topological insulators

- 1D and 2D strongly interacting frustrated spin lattice systems

- Kitaev 2D spin-interacting model

- Iron-based superconductors

- Quasi-1D and 2D superconducting frustrated arrays of quantum Josephson junctions/qubits