Explore the challenges and potential of Neural Quantum States in representing complex quantum many-body systems through this comprehensive lecture. Delve into the exponential complexity of quantum state representation and the limitations of traditional tensor network approaches. Examine the emerging field of neural quantum states and their ability to represent volume law quantum states. Investigate the application of multi-layer feed-forward networks to find ground states with volume-law entanglement entropy, using the Sachdev-Ye-Kitaev model as a testbed. Discover the limitations of both shallow and deep feed-forward networks in representing complex quantum states, highlighting the need for further research into efficient neural representations of physical quantum states. Gain insights into various topics including entanglement entropy, matrix product states, single neural layers, neural networks, tensor networks, and modified Allen Chester models.
Neural Quantum States Approach to Volume Law Ground States
PCS Institute for Basic Science via YouTube
Overview
Syllabus
Introduction
Presentation
General introduction
The problem
The bypass
Scaling behavior
Entanglement entropy
Matrix product states
Quantum states for physical systems
Neural Quantum States
Single Neural Layer
Neural Network
Kalia
Tensor Networks
Modified Allen Chester Model
Original results
S5K model
S4K model
Neural Quantum State
Taught by
PCS Institute for Basic Science
