Machine Learning for Quantum Many-Body Systems

Contribution List

3. Welcome and Opening Remarks

Roger Melko (Perimeter Institute & University of Waterloo)

6/12/23, 9:55 AM

ZOOM: https://pitp.zoom.us/j/94595394881?pwd=OUZSSXpzYlhFcGlIRm81Y3VaYVpCQT09

4. [Virtual] Exploring Quantum Science with Machine Learning

Di Luo (Massachusetts Institute of Technology)

6/12/23, 10:00 AM

ZOOM: https://pitp.zoom.us/j/94595394881?pwd=OUZSSXpzYlhFcGlIRm81Y3VaYVpCQT09

5. 2D quantum matter with neural quantum states

Markus Heyl (Max Planck Institute for the Physics of Complex Systems)

6/12/23, 11:15 AM

ZOOM: https://pitp.zoom.us/j/94595394881?pwd=OUZSSXpzYlhFcGlIRm81Y3VaYVpCQT09

Neural quantum states (NQSs) have emerged as a novel promising numerical method to solve the quantum many-body problem. However, it has remained a central challenge to train modern large-scale deep network architectures to desired quantum state accuracy, which would be vital in
utilizing the full power of NQSs...

6. Near Term Distributed Quantum Computation using Optimal Auxiliary Encoding

Abigail McClain Gomez (Harvard University)

6/12/23, 2:00 PM

ZOOM: https://pitp.zoom.us/j/94595394881?pwd=OUZSSXpzYlhFcGlIRm81Y3VaYVpCQT09

7. Dimension reduction of the Functional Renormalization Group

Jiawei Zang (Columbia University)

6/12/23, 3:15 PM

ZOOM: https://pitp.zoom.us/j/94595394881?pwd=OUZSSXpzYlhFcGlIRm81Y3VaYVpCQT09

In this work, we use data-driven methods to reduce the dimensionality of the vertex function for the Hubbard model and spin liquid model. By employing a deep learning architecture based on the autoencoder, we show that the functional renormalization group (FRG) dynamics can be efficiently learned. Our approach is...

8. Neural quantum states for simulating strongly interacting fermions in continuous space

Jannes Nys ( École Polytechnique Fédérale de Lausanne)

6/12/23, 3:45 PM

ZOOM: https://pitp.zoom.us/j/94595394881?pwd=OUZSSXpzYlhFcGlIRm81Y3VaYVpCQT09

We introduce a novel neural quantum state architecture for the accurate simulation of extended, strongly interacting fermions in continuous space. The variational state is parameterized via permutation equivariant message passing neural networks to transform single-particle coordinates to highly correlated...

9. Automated Characterization of Engineered Quantum Materials

Eliška Greplová (Delft University)

6/13/23, 10:00 AM

10. Learning Feynman Diagrams with Tensor Trains

Xavier Waintal (CEA Grenoble)

6/13/23, 11:15 AM

25. Data-centric learning of Quantum Many-body States with Classical Machines

Eun-Ah Kim (Cornell University)

6/13/23, 1:30 PM

12. Quantum and Classical Dynamics from a Time Dependent Variational Principle

Moritz Reh (Heidelberg University)

6/13/23, 2:45 PM

13. A QMC study of the Rydberg phase diagram

Anna Knörr (Perimeter Institute)

6/13/23, 3:15 PM

29. Colloquium

Neil Turok (University of Edinburgh)

6/13/23, 4:00 PM

17. [VIRTUAL] A deep variational free energy approach to dense hydrogen

Lei Wang (Chinese Academy of Sciences)

6/14/23, 10:00 AM

Dense hydrogen, the most abundant matter in the visible universe, exhibits a range of fascinating physical phenomena such as metallization and high-temperature superconductivity, with significant implications for planetary physics and nuclear fusion research. Accurate prediction of the equations of state and phase diagram of dense hydrogen has long been a challenge for computational methods....

16. Quantum-enhanced reinforcement learning

Valeria Saggio (Massachusetts Institute of Technology)

6/14/23, 11:30 AM

The field of artificial intelligence (AI) has experienced major developments over the last decade. Within AI, of particular interest is the paradigm of reinforcement learning (RL), where autonomous agents learn to accomplish a given task via feedback exchange with the world they are placed in, called an environment. Thanks to impressive advances in quantum technologies, the idea of using...

14. Unsupervised detection of quantum phases and their order parameters from projective measurements

Anna Dawid (Flatiron Institute)

6/14/23, 2:00 PM

Recently, machine learning has become a powerful tool for detecting quantum phases. While the sole information about the presence of transition is valuable, the lack of interpretability and knowledge on the detected order parameter prevents this tool from becoming a customary element of a physicist's toolbox. Here, we report designing a special convolutional neural network with adaptive...

15. Investigating Topological Order with Recurrent Neural Network Wave Functions

Mohamed Hibat Allah (Vector Institute)

6/14/23, 2:30 PM

Recurrent neural networks (RNNs), originally developed for natural language processing, hold great promise for accurately describing strongly correlated quantum many-body systems. In this talk, we will illustrate how to use 2D RNNs to investigate two prototypical quantum many-body Hamiltonians exhibiting topological order. Specifically, we will demonstrate that RNN wave functions can...

18. Ethics PROBES: Artificial Intelligence, Machine Learning, & Quantum Computing

Gus Skorburg (University of Guelph)

6/14/23, 3:30 PM

19. Discussion Session: The Ethics of Quantum and AI

Gus Skorburg (University of Guelph)

6/14/23, 4:15 PM

20. [VIRTUAL] Emergent Classicality from Information Bottleneck

Yi-Zhuang You (University of California, San Diego)

6/15/23, 10:00 AM

Our universe is quantum, but our everyday experience is classical. Where is the boundary between quantum and classical worlds? How does classical reality emerge in quantum many-body systems? Does the collapse of the quantum states involve intelligence? These are fundamental questions that have puzzled physicists and philosophers for centuries. The recent development of quantum information...

22. Talk - tbc

Stefanie Czischek (University of Ottawa)

6/15/23, 11:15 AM

23. Panel Discussion

Edwin Miles Stoudenmire ( Flatiron Institute Center for Computational Quantum Physics), Eliška Greplová (Delft University), Juan Felipe Carrasquilla (Vector Institute), Mykola Maksymenko (Haiqu)

6/15/23, 2:00 PM

27. Machine Learning of Conserved Quantities and Symmetry Invariants

Sebastian Wetzel (University of Waterloo)

6/15/23, 4:00 PM

28. Solving 2D quantum matter with neural quantum states

Markus Heyl (Max Planck Institute for the Physics of Complex Systems)

6/15/23, 4:45 PM

Neural quantum states (NQSs) have emerged as a novel promising numerical method to solve the quantum many-body problem. However, it has remained a central challenge to train modern large-scale deep network architectures to desired quantum state accuracy, which would be vital in utilizing the full power of NQSs and making them competitive or superior to conventional numerical approaches. Here,...

24. The Quantum Cartpole

Evert van Nieuwenburg (Leiden University)

6/16/23, 10:00 AM

How do you control something you can not look at? For controlling quantum systems, information on the system’s state could come through weak measurements. Such measurements provide some information, but will inevitably also perturb the system, meaning there is noise both in the state estimation as well as in the measurement. We study a simple single particle quantum setup (the quantum...

11. Quantum HyperNetworks: Training Binary Neural Networks in Quantum Superposition

Estelle Inack (Perimeter Institute)

6/16/23, 11:15 AM

Binary neural networks, i.e., neural networks whose parameters and activations are constrained to only two possible values, offer a compelling avenue for the deployment of deep learning models on energy- and memory-limited devices. However, their training, architectural design, and hyperparameter tuning remain challenging as these involve multiple computationally expensive combinatorial...

26. Closing Remarks

Roger Melko (Perimeter Institute & University of Waterloo)

6/16/23, 12:10 PM