Tensor Networks and Neural Network States - From Chiral Topological Order to Image Classification

Explore the intersection of quantum physics and machine learning in this 28-minute conference talk by Ignacio Cirac from the Max Planck Institute for Quantum Optics. Delve into the application of neural networks for enhancing image recognition, covering topics such as quantum many-body physics, tensor networks, and string bond states. Discover how concepts from physics are applied to machine learning problems, including area law, bond dimensions, and Monte Carlo methods. Learn about the potential of these techniques for solving quantum many-body problems and their applications in sound recognition. Gain insights into the future directions of this interdisciplinary field, bridging the gap between quantum physics and artificial intelligence.

Introduction
Quantum Manybody Physics
Manual Physics
Machine Learning
Area Law
Typical Stage
Tension
Tensors
Bond Dimensions
Monte Carlo Methods
String Bond State
Quantum Manybody Problems
String Bond States
Results
Sound Recognition
Future Work