Loading [MathJax]/extensions/TeX/boldsymbol.js
Machine Learning meets Quantum Physics
Contents
What is this talk about?
Quantum Computing and Machine Learning
More material on Machine Learning and Quantum Mechanics
Why? Basic motivation
Overview
Machine Learning and AI and Physics
A new world
Lots of room for creativity
Types of Machine Learning
A simple perspective on the interface between ML and Physics
ML in Physics, Examples
More examples
From Classical Computations to Quantum Data and Quantum Machine Learning
Selected References in Machine Learning
References on Quantum Machine Learning
Applications of Quantum Machine Learning
What are the Machine Learning calculations here based on?
A Frequentist approach to data analysis
What are the basic ingredients?
Artificial neurons
A simple perceptron model
Neural network types
Hybrid Quantum Computing and Machine Learning Network
The first system: 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
Energy derivatives
Derivatives of the local energy
Why Boltzmann machines?
A standard BM setup
The structure of the RBM network
The network
Goals
Joint distribution
Defining different types of RBMs
Representing the wave function
Choose the cost/loss function
Running the codes
Energy as function of iterations,
N=2
electrons
Onebody densities
N=30
,
\hbar\omega=1.0
a.u.
Onebody densities
N=30
,
\hbar\omega=0.1
a.u.
Or using Deep Learning Neural Networks
Replacing the Jastrow factor with Neural Networks
And then Quantum Boltzmann Machines
Selected Results from "Zoufal et al.":"https://arxiv.org/abs/2006.06004"
Conclusions and where do we stand
Onebody densities
N=30
,
\hbar\omega=1.0
a.u.
«
1
...
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
»
