Neural Network Quantum States

In course

Description

This lecture introduces the concept of neural network quantum states, focusing on parameterizing wave function amplitudes with neural networks. The instructor explains the use of complex valued functions, the importance of representing amplitudes with logs, and the process of training neural networks to minimize a loss function. The lecture also covers the application of neural network quantum states in solving complex spin models, exact sampling techniques, and comparisons with traditional methods in chemistry problems.

Instructors (7)

Official source

https://mediaspace.epfl.ch/media/0_6xfjz6ry

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Recurrent neural network

A recurrent neural network (RNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers. In contrast to uni-directional feedforward neural network, it is a bi-directional artificial neural network, meaning that it allows the output from some nodes to affect subsequent input to the same nodes. Their ability to use internal state (memory) to process arbitrary sequences of inputs makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition.

Artificial neural network

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Types of artificial neural networks

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