Recurrent neural networks for many-body physics

ORAL  · Invited

Abstract

I will discuss our recent work on the use of autoregressive neural networks for many-body physics. In particular, I will discuss two approaches to represent quantum states using these models and their applications to the reconstruction of quantum states, the simulation of real-time dynamics of open quantum systems, and the approximation of ground states of many-body systems displaying long-range order, frustration, and topological order. Finally, I will discuss how annealing in these systems can be used for combinatorial optimization where we observe solutions to problems that are orders of magnitude more accurate than simulated and simulated quantum annealing.

*Juan Carrasquilla acknowledges support from the Natural Sciences and Engineering Research Council (NSERC), the Shared Hierarchical Academic Research Computing Network (SHARCNET), Compute Canada, and the Canadian Institute for Advanced Research (CIFAR) AI chair program. Resources used in preparing this research were provided, in part, by the Province of Ontario, the Government of Canada through CIFAR, and companies sponsoring the Vector Institute www.vectorinstitute.ai/#partners

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Publication: -Recurrent neural network wave functions. M Hibat-Allah, M Ganahl, LE Hayward, RG Melko, J Carrasquilla. Physical Review Research 2 (2), 023358 (2020)
-Reconstructing quantum states with generative models. J Carrasquilla, G Torlai, RG Melko, L Aolita. Nature Machine Intelligence 1 (3), 155-161 (2019)
-Variational neural annealing. M Hibat-Allah, EM Inack, R Wiersema, RG Melko, J Carrasquilla. Nature Machine Intelligence 3 (11), 952-961 (2021)
-Autoregressive neural network for simulating open quantum systems via a probabilistic formulation
D Luo, Z Chen, J Carrasquilla, BK Clark. Physical review letters 128 (9), 090501 (2022)

Presenters

  • Juan Carrasquilla

    • Vector Institute for Artificial Intelligence

Authors

  • Juan Carrasquilla

    • Vector Institute for Artificial Intelligence