Many-body quantum physics with machine learning

4 Sept 2023, 09:00 → 8 Sept 2023, 18:00 Europe/Rome

Aula Renzo Leonardi (ECT*)

Alessandro Lovato (Argonne National Laboratory, United States & Trento Institute for Fundamental & Applied Physics (TIFPA)), Arnau Rios Huguet (Institute of Cosmos Sciences, University of Barcelona, Spain), Estelle Inack (Perimeter Institute for Theoretical Physics & yiyaniQ Inc., Canada), Giuseppe Carleo (École polytechnique Fédérale de Lausanne (EPFL), Switzerland)

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Machine learning techniques have become standard scientific tools across several fields. Quantum many-body theory is no exception, with a recent explosion of applications in domains that range from spin systems, to quantum chemistry and nuclear physics. This workshop is devoted to discussing machine-learning tools that aim at directly solving the Schrödinger equation in a many-particle context. These tools typically exploit the outstanding variational properties of neural networks, including first- and second-quantized versions. We aim at bringing together quantum many-body practitioners, within and outside the nuclear domain, and physics-focused machine learning experts, to review past achievements and recent advances in solving quantum many-body problems with machine learning tools. By bringing together different communities, we want to take a global look at the state-of-the-art in the field; to identify new avenues and recent meaningful advances; and to stimulate cross-fertilisation among fields to consolidate the use of machine-learning tools in the many-body domain.

       

Monday 4 September

1 REGISTRATION

2 Welcome ECT* Director

Speaker: Gert Aarts

3 Workshop Introduction

Speaker: Arnau Rios Huguet

10:30 COFFEE BREAK

4 Electron gas and hydrogen: from ground state energies to excitation spectra

Speaker: Markus Holzmann

talk2.pdf

5 Learning ground states of gapped quantum Hamiltonians with Kernel Methods

Speaker: Clemens Giuliani

12:30 LUNCH

6 Neural Quantum States for Mixed States and their sample complexity

Speaker: Filippo Vicentini

7 Liquid-Solid Phase Transition in 2D He-4 With Neural Quantum States

Speaker: David Linteau

linteau_slides.pptx

15:30 COFFEE BREAK

8 Discussion session

Tuesday 5 September

9 A simple NQS on a discretized lattice space

Speaker: Carlo Barbieri

ECT_Sept2023_NQS_barbieri.pdf

10:30 Coffee break

10 Neural Network Quantum States for spinless fermions

Speaker: James Keeble

Neural_Network_Quantum_States_for_Spinless_Fermions.pdf

11 Anti-symmetrized neural quantum states

Speaker: Javier Rozalén

ECT Workshop Presentation-JAVIS-LAPTOP.pptx

12:30 LUNCH

12 Ultracold atoms with neural network quantum states

Speaker: Dr Jane Kim (OSU)

JaneKim_ECT.pdf

13 Transfer learning for many-body physics

Speaker: Amir Azzam

con_transfer_learning.pptx

15:30 COFFEE BREAK

14 Discussion session

Wednesday 6 September

15 Neural Quantum States for Neutron Stars

Speaker: Bryce Fore

Bryce_Fore_Trento.pdf

10:30 COFFEE BREAK

16 Natural Quantum Monte Carlo Computation of Excited States

Speaker: David Pfau

Trento Talk - September 2023.pdf

17 Neural Network Applications in Pionless EFT

Speaker: Elad Parnes

12:30 LUNCH

18 ML applied to the analysis of quantum phases in a quantum simulation of the Agassi model

Speaker: Pedro Pérez-Fernandez

Trento23_PPF.pdf

19 Unbiasing time-dependent Variational Monte Carlo by projected quantum evolution

Speaker: Alessandro Sinibaldi

Sinibaldi_ECT.pptx

15:30 COFFEE BREAK

20 Discussion session

Thursday 7 September

21 Optimising Fermionic Neural Networks with Decision Geometry

Speaker: Mehdi Drissi

10:30 COFFEE BREAK

22 Self-learning projective quantum Monte Carlo simulations guided by neural network states

Speaker: Sebastiano Pilati

Pilati_ECTStar.pdf

23 Evolution optimization in Digital-Analog Quantum Simulations

Speaker: Alvaro Saiz-Castillo

Evolution optimization in Digital-Analog Quantum Simulations.pdf

12:30 LUNCH

24 Simulating Quantum Field Theories with Neural Network Representation

Speaker: Di Luo

Simulating Quantum Field Theories with Neural Network Representation_ect062223_2.pdf

25 Machine learning flow-based models for nuclear many-body calculations with quantified uncertainties

Speaker: Pengsheng Wen (Texas A&M University)

flow-based_generative_model.pptx

15:30 COFFEE BREAK

26 Discussion session

Friday 8 September

27 Improving qubit readout with autoencoders

Speaker: Dr Piero Luchi (Trento)

Talk Many Body Quantum systems and Machine Learning - Copia.pptx

10:30 COFFEE BREAK

28 Data-driven discovery of relevant information in quantum simulators

Speaker: Roberto Verdel Aranda (The Abdus Salam International Centre for Theoretical Physics (ICTP))

20230908_ECT__RVA.pdf

29 Exploring self-bound Hypernuclear states with Neural Quantum States

Speaker: Andrea Di Donna

12:30 LUNCH