Distributed Training for Deep Reinforcement Learning Decoders on the Toric Code

dc.contributor.authorOlsson, Adam
dc.contributor.authorLindeby, Gabriel
dc.contributor.departmentChalmers tekniska högskola / Institutionen för fysiksv
dc.contributor.examinerGranath, Mats
dc.contributor.supervisorGranath, Mats
dc.date.accessioned2020-06-23T10:31:52Z
dc.date.available2020-06-23T10:31:52Z
dc.date.issued2020sv
dc.date.submitted2020
dc.description.abstractWe 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.sv
dc.identifier.coursecodeTIFX05sv
dc.identifier.urihttps://hdl.handle.net/20.500.12380/300977
dc.language.isoengsv
dc.setspec.uppsokPhysicsChemistryMaths
dc.subjectDeep Reinforcement Learningsv
dc.subjectDistributedsv
dc.subjectToric Codesv
dc.subjectQuantum Error Correctionsv
dc.subjectApe-Xsv
dc.titleDistributed Training for Deep Reinforcement Learning Decoders on the Toric Codesv
dc.type.degreeExamensarbete för masterexamensv
dc.type.uppsokH
Ladda ner
Original bundle
Visar 1 - 1 av 1
Bild (thumbnail)
Namn:
Master_Thesis_20_Lindeby_Olsson.pdf
Storlek:
2.1 MB
Format:
Adobe Portable Document Format
Beskrivning:
License bundle
Visar 1 - 1 av 1
Bild saknas
Namn:
license.txt
Storlek:
1.14 KB
Format:
Item-specific license agreed upon to submission
Beskrivning: