CONVENER

Victor Kagalovsky (Shamoon College of Engineering, Israel)

MEMBERS

Alexei Andreanov (PCS IBS)

Mikhail Fistul (Ruhr-University Bochum, Germany)

Ara Go (PCS IBS)

Woo Seok Lee (PCS IBS)

Eun-Gook Moon (KAIST, Korea)

Tomi Ohtsuki (Sophia University, Japan)

Miguel Ortuño (University of Murcia, Spain)

David Saad (Aston University, UK)

Keith Slevin (Osaka University, Japan)

Igor Yurkevich (Aston University, UK)

VISITORS

Aleksey Fedorov (Russian Quantum Center, Russia)

Sergey Kravchenko (Northeastern University, USA)

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