Machine Learning for Many-Body Quantum Systems

Stochastic numerical methods for many-body quantum systems

Explores stochastic methods for quantum systems, including exact diagonalization, variational methods, neural networks, and machine learning.

Optical Machine Learning: Harnessing Multiple Scattering for Efficiency

Explores the use of optics in machine learning, focusing on large-scale random matrix multiplication through multiple scattering of light.

Eigenstate Thermalization Hypothesis

Explores the Eigenstate Thermalization Hypothesis in quantum systems, emphasizing the random matrix theory and the behavior of observables in thermal equilibrium.

Variational Method in Relativistic Quantum Field Theory

Delves into the variational method in relativistic quantum field theory without cutoff, emphasizing weakly entangled states and the transition to relativistic continuous matrix product states.

Statistical Physics in Machine Learning: Understanding Deep Learning

Explores the application of statistical physics in understanding deep learning with a focus on neural networks and machine learning challenges.