Master presentation: Valdemar Bergentall
Science and Information Technology
Presentation of master thesis. The title of the master thesis is "Quantum error correction using graph neural networks".
Examination
Presentation of master thesis. The title of the master thesis is "Quantum error correction using graph neural networks".
Supervisor: Mats Granath
Examiner: Johannes Hofmann
Opponent: Gabriel Angerd
A graph neural network (GNN) is constructed and trained with a purpose of using it as a quantum error correction decoder for depolarized noise on the surface code. Since associating syndromes on the surface code with graphs instead of grid-like data seemed promising, a previous decoder based on the Markov Chain Monte Carlo method was used to generate data to create graphs. In this thesis the emphasis has been on error probabilities, p = 0.05, 0.1 and surface code sizes d = 5, 7, 9. Two specific network architectures have been tested using various graph convolutional layers. While training the networks, evenly distributed datasets were used and the highest reached test accuracy for p = 0.05 was 97% and for p = 0.1 it was 81.4%. Utilizing the trained network as a quantum error correction decoder for p = 0.05 the performance did not achieve an error correction rate equal to the reference algorithm Minimum Weight Perfect Matching. Further research could be done to create a custom-made graph convolutional layer designed with intent to make the contribution of edge attributes more pivotal.
Zoom-link: https://chalmers.zoom.us/j/67725530656
Password: 911476