Machine Learning for Many-Body Quantum Systems

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (32)

Page 1 of 4

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.

Neural Networks: Two Layers Neural Network

Covers the basics of neural networks, focusing on the development from two layers neural networks to deep neural networks.

State Space Models: Expressivity and Transformers

Covers state space models and their expressivity compared to transformers, focusing on attention mechanisms and computational efficiency.

Deep Learning: Principles and Applications

Covers the fundamentals of deep learning, including data, architecture, and ethical considerations in model deployment.

Unsupervised Learning: Clustering & Dimensionality Reduction

Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.

Neural Quantum States

Explores neural quantum states and their representation using artificial neural networks.