Bulletin of the American Physical Society
APS March Meeting 2019
Volume 64, Number 2
Monday–Friday, March 4–8, 2019; Boston, Massachusetts
Session H18: Machine Learning Quantum States III
2:30 PM–5:06 PM,
Tuesday, March 5, 2019
BCEC
Room: 156B
Sponsoring
Units:
DCOMP DCMP DAMOP
Chair: Juan Carrasquilla, Vector Institute
Abstract: H18.00003 : The recoverable quantum information: A smart reward for reinforcement learning of quantum error correction
3:18 PM–3:30 PM
Presenter:
Thomas Foesel
(Max Planck Institute for the Science of Light)
Authors:
Thomas Foesel
(Max Planck Institute for the Science of Light)
Petru Tighineanu
(Max Planck Institute for the Science of Light)
Talitha Weiss
(IQOQI, University of Innsbruck)
Florian Marquardt
(Max Planck Institute for the Science of Light)
Beyond the possibility to judge how well existing techniques perform, it is particularly useful for the autonomous discovery of new quantum error correction strategies, by using it as an immediate reward scheme for a reinforcement learning approach as demonstrated in [1]. The recoverable quantum information is applicable generically, i.e. independent of the concrete hardware platform, noise model, etc. In this talk, this quantity will be introduced and motivated, an intuitive interpretation is given, it is shown how it evolves in some exemplary systems, and I will sketch how it can be computed numerically.
[1] T. Fösel, P. Tighineanu, T. Weiss, and F. Marquardt, Phys. Rev. X 8, 031084 (2018)
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