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2017

Quantum fields as deep learning

Lee, Jae-Weon

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In this essay we conjecture that quantum fields such as the Higgs field is related to a restricted Boltzmann machine for deep neural networks.

An accelerating Rindler observer in a flat spacetime sees the quantum fields having a thermal distribution from the quantum entanglement, and a renormalization group process for the thermal fields on a lattice is similar to a deep learning algorithm.
This correspondence can be generalized for the KMS states of quantum fields in a curved spacetime like a black hole.

Quantum fields as deep learning · Around

Built on

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Similar

J.-W. Lee, J. Lee, and H.-C. Kim, JCAP08(2007)005 (2007), eprint hep-th/0701199
2007
Cited alongside, same era.
M. Van Raamsdonk, Gen. Rel. Grav. 42
2010
Cited alongside, same era.
J.-W. Lee, H.-C. Kim, and J. Lee, J. Korean Phys. Soc. 63
2013
Cited alongside, same era.
P. Baldi, P. Sadowski, and D. Whiteson, Nature Commun. 5
2014
Cited alongside, same era.

Then

P. Mehta and D. J. Schwab, ArXiv e-prints (2014), eprint 1410.3831
2014
Later among the works it cites.
M. Van Raamsdonk, in Proceedings, Theoretical Advanced Study Institute in Elementary Particle Physics: New Frontiers in Fields and Strings (TASI 2015): Boulder, CO, USA, June 1-26, 2015 (2017), pp. 297–351, eprint 1609.00026
2017
Closest in time.
W.-C. Gan and F.-W. Shu, arXiv:1705.05750 (2017)
Original
2017
Closest in time.
J.-W. Lee, arXiv:1704.05057 (2017)
Original
2017
Closest in time.

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