University of Cambridge > Talks.cam > Machine learning in Physics, Chemistry and Materials discussion group (MLDG) > Neural network quantum states, from lattice models to quantum chemistry and quantum computing

Neural network quantum states, from lattice models to quantum chemistry and quantum computing

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In this seminar I will present a fairly recent application of machine learning to quantum physics, more specifically, the task to machine learn many-body quantum states.

First I will introduce the seminal work [1] where a variational representation of quantum states based on artificial neural networks has been devised.

Then I will show how such compact representation can be used to perform unsupervised learning type of tasks, such as quantum state tomography [2], i.e. how to characterise a quantum state from a limited number of simple experimental measurements. As an example, I will show how this can benefit already existing quantum technologies, i.e. in providing order-of-magnitude speed-up for certain tasks ubiquitous in variational quantum computation [3]. Finally I will briefly overview very recent efforts to solve the Schrödinger equation for Fermionic systems with shallow and deep neural networks [4-5].

[1] Carleo & Troyer, Science 355 (6325), 602-606 (2017)

[2] Torlai et. al. Nature Physics 14 (5), 447-450 (2018)

[3] Torlai et. al. Physical Review Research 2 (2), 022060 (2020)

[4] Choo et. al. Nature communications 11 (1), 1-7 (2020)

[5] Pfau et. al. arXiv:1909.02487 (2019)

This talk is part of the Machine learning in Physics, Chemistry and Materials discussion group (MLDG) series.

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