QPhML2020 Day 1

0:04:15

Introduction and Welcome

0:41:13

Neural autoregressive toolbox for many-body physics

0:38:38

Variational Learning of Many-Body Quantum Systems

0:44:32

Probabilistic Modelling with Tensor Networks - A Bridge from Graphical Models to Quantum Circuits

0:39:52

Quantum Machine Learning Algorithms for Knowledge Graphs

0:39:26

Self-learning machines based on nonlinear field evolution

0:41:46

Deep neural network solution of the electronic Schrödinger equation

0:42:29

Quantum Deformed Binary Neural Networks

0:33:40

Learning quantum models from quantum or classical data

0:28:26

Quantum Computing and Machine Learning: How two technologies could enable a new age in chemistry

0:39:28

Machine learning the quantum mechanics of materials and molecules

0:37:10

Analysing the dynamics of message passing algorithms using statistical mechanics

0:37:27

Quantum annealing and machine learning - learning and Black-box optimization

0:42:21

Small quantum computers and big classical data sets

0:38:35

Reinforcement Learning assisted Quantum Optimization

0:35:43

Mix and Match: leveraging optically-created random embeddings in Machine Learning pipelines

0:41:14

Symmetry, locality and long-range interactions in atomistic machine learning

0:43:50

Learning and AI in the quantum domain

0:37:44

The role of data structure in learning in shallow neural networks

0:38:07

Toward quantum advantages for topological data analysis

0:41:28

Quantum Machine Learning in Chemical Compound Space

0:42:06

Eq. informed and data-driven tools for data-assimilation and data-classification of turbulent flows

0:44:51

Replica analysis of overfitting in generalized linear regression models

0:37:51

Unifying Quantum Chemistry and Machine Learning