Adam Olsson and Gabriel Lindeby, MPALG

​Title of Master's thesis: Distributed Training for Deep Reinforcement Learning Decoders on the Toric Code

Follow the presentation online​
Meeting ID: 647 6075 6824
Password: 469819​

​Abstract:

The inherently fragile nature of quantum bits, called qubits, cause unavoidable errors in quantum computations. To avoid these errors, qubits need to constantly be monitored and corrected. However, the correction process, called decoder, can not directly observe the error. Instead, the decoder needs to incorporate statistics into the correction process. Recently, deep reinforcement learning has been suggested as a tool for quantum error correction because of its ability to learn advanced control policies from sensory input. These reinforcement learning algorithms require large amounts of data which takes a long time to produce. In this thesis, we implement a distributed framework to train a decoder based on deep reinforcement learning and that produces the same results in less than half of the wall clock time.
​Supervisor and examiner: Mats Granath
Opponents: Tobias Sandström & Lars Jansson
Category Student project presentation
Location: Online via Zoom
Starts: 28 May, 2020, 10:00
Ends: 28 May, 2020, 11:00

Published: Tue 19 May 2020.