Decoding small surface codes with feedforward neural networks

Journal article (2018)

Authors

S. Varsamopoulos FTQC/Bertels Lab

D.B. Criger QCD/Terhal Group - QuTech Advanced Research Centre

K.L.M. Bertels FTQC/Bertels Lab

Research Group

FTQC/Bertels Lab

Artificial neural networks Fault tolerance Quantum error correction Surface codes

To reference this document use:

http://resolver.tudelft.nl/uuid:2157fc7a-569d-436d-9d0a-f8ef12ceb18e

More Info

expand_more

Published Date

01-01-2018

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Research Group

FTQC/Bertels Lab

Abstract

Surface codes reach high error thresholds when decoded with known algorithms, but the decoding time will likely exceed the available time budget, especially for near-term implementations. To decrease the decoding time, we reduce the decoding problem to a classification problem that a feedforward neural network can solve. We investigate quantum error correction and fault tolerance at small code distances using neural network-based decoders, demonstrating that the neural network can generalize to inputs that were not provided during training and that they can reach similar or better decoding performance compared to previous algorithms. We conclude by discussing the time required by a feedforward neural network decoder in hardware.