Machine learning techniques are rapidly being adopted into the field of quantum manybody physics including condensed matter theory experiment and quantum information science. The steady increase in data being produced by highlycontrolled quantum experiments brings the potential of machine learning algorithms to the forefront of scientific advancement. Particularly exciting is the prospect of using machine learning for the discovery and design of quantum materials devices and computers. In order to make progress the field must address a number of fundamental questions related to the challenges of studying manybody quantum mechanics using classical computing algorithms and hardware. The goal of this conference is to bring together experts in computational physics machine learning and quantum information to make headway on a number of related topics including: Datadrive quantum state reconstruction Machine learning strategies for quantum error correction Neuralnetwork based wavefunctions Nearterm prospects for data from quantum devices Machine learning for quantum algorithm discovery Registration for this event is now closed
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Simulating Thermal and Quantum Fluctuations in Materials and Molecules
Michele Ceriotti L'Ecole Polytechnique Federale de Lausanne (EPFL)

How to use a Gaussian Boson Sampler to learn from graphstructured data
Maria Schuld University of KwaZuluNatal

Machine learning meets quantum physics
DongLing Deng Tsinghua University


Engineering Programmable Spin Interactions in a NearConcentric Cavity
Emily Davis Stanford University

Alleviating the sign structure of quantum states
Giacomo Torlai Flatiron Institute

Navigating the quantum computing field as a high school student
Tanisha Bassan The Knowledge Society

Machine Learning Quantum Emergence from Complex Data
EunAh Kim Cornell University



Quantum Error Correction via Hamiltonian Learning
Eliska Greplova Delft University of Technology