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Neural network representation of quantum systems

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If you have a question about this talk, please contact Sven Krippendorf .

We provide a novel map with which a wide class of quantum mechanical systems can be cast into the form of a neural network with a statistical summation over network parameters. Our simple idea is to use the universal approximation theorem of neural networks to generate arbitrary paths in the Feynman’s path integral. The map can be applied to interacting quantum systems / field theories, even away from the Gaussian limit. Our findings bring machine learning closer to the quantum world. The talk is based on a collaboration with Y. Hirono, J. Maeda and J. Totsuka-Yoshinaka, https://arxiv.org/abs/2403.11420

This talk is part of the DAMTP Data Intensive Science Seminar series.

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Keynes Hall, Kings College, Cambridge Webinar & FW11, Computer Laboratory, William Gates Building. HCR + ONLINE - Details to be sent by email

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