Solving quantum mechanical problems with Machine Learning
Contents
What is this talk about?
Why?
Overview
What are the Machine Learning calculations here based on?
A new world
Lots of room for creativity
Knowledge of Statistical analysis and optimization of data
Some members of the ML family
What are the basic ingredients?
What is Machine Learning?
Types of Machine Learning
References
Another interesting article
Courses on Data science and Machine Learning at UiO
Additional courses of interest
Decision trees and Regression
Artificial neurons
A simple perceptron model
Neural network types
The system: two electrons in a harmonic oscillator trap in two dimensions
Quantum Monte Carlo Motivation
Quantum Monte Carlo Motivation
Quantum Monte Carlo Motivation
Quantum Monte Carlo
The trial wave function
The correlation part of the wave function
Resulting ansatz
The VMC code
Technical aspect, improvements and how to define the cost function
Energy derivatives
Derivatives of the local energy
How do we define our cost function?
Meet the variance and its derivatives
The variance defines the cost function
Why Boltzmann machines?
Boltzmann Machines
Some similarities and differences from DNNs
Boltzmann machines (BM)
A standard BM setup
The structure of the RBM network
The network
Goals
Joint distribution
Network Elements, the energy function
Defining different types of RBMs
More about RBMs
Sampling: Metropolis sampling
Sampling: Gibbs sampling
Gaussian RBM
Cost function
Optimization / Training
More on RBMs
Which sampling to use
RBMs for the quantum many body problem
Choose the right RBM
Representing the wave function
Choose the cost function
Running the codes
Energy as function of iterations, \( N=2 \) electrons
Energy as function of iterations, \( N=6 \) electrons
Conclusions and where do we stand
Additional material
Kullback-Leibler relative entropy
Optimizing the cost function
Setting up for gradient descent calculations
More interpretations
Energy as function of iterations, \( N=6 \) electrons
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