Distributed Training for Deep Reinforcement Learning Decoders on the Toric Code
Typ
Examensarbete för masterexamen
Program
Publicerad
2020
Författare
Olsson, Adam
Lindeby, Gabriel
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
We distribute the training of a deep reinforcement learning-based decoder on the
toric code developed by Fitzek et al. [9]. Reinforcement learning agents asynchronously
step through multiple environments in parallel and store transitions in
a prioritized experience replay buffer. A separate process samples the replay buffer
and performs backpropagation on a policy network. With this setup, we managed
to improve wall-clock training times with a factor 12 for toric code sizes of d = 5
and d = 7. For d = 9, we were unable to reach optimal performance but improved
the decoder’s success rate using a network with a parameter reduction of factor
20. We argue that these results pave the way for optimal decoders, correcting errors
close to what is theoretically possible, based on reinforcement learning for toric
code sizes ≤ 9. The complete code for the training and toric code environment can
be found in the repository https://github.com/Lindeby/toric-RL-decoder and
https://github.com/Lindeby/gym_ToricCode.
Beskrivning
Ämne/nyckelord
Deep Reinforcement Learning , Distributed , Toric Code , Quantum Error Correction , Ape-X