Fermionic Neural-Network Quantum States - IPAM at UCLA

Explore fermionic neural-network quantum states in this 43-minute conference talk presented by Giuseppe Carleo from École Polytechnique Fédérale de Lausanne at IPAM's Monte Carlo and Machine Learning Approaches in Quantum Mechanics Workshop. Delve into topics such as neural-network quantum states, universal approximation theorem, efficient representations, and learning weights. Examine the state of the art in fermions, first quantization, and Eden fermions. Gain insights into the latest results and future outlook in this field of quantum mechanics and machine learning.

Introduction
Neuralnetwork quantum states
Universal approximation theorem
Efficient representations
Learning weights
State of the art
Fermions
First quantization
Eden fermions
Results
Outlook