Machine learning for topological codes and topological matter

NAISS 2023/5-353


NAISS Medium Compute

Principal Investigator:

Mats Granath


Göteborgs universitet

Start Date:


End Date:


Primary Classification:

10304: Condensed Matter Physics



Machine learning applications in physics is a rapidly expanding and very broad field. We have been working on two different machine learning projects in quantum physics. The first uses deep reinforcement learning for topological error correcting codes and for quantum circuit optimization. Quantum error correction is related to making quantum computers robust against noise, while quantum circuit optimization is related to programming a quantum computer, which corresponds to mapping an abstract quantum operation to the actual hardware. The work on quantum computing is funded by the Wallenberg Centre for Quantum Technology and a recent grant from SSF, Quantum Stack: Programming the Quantum Computer. Collaborators in this project, in addition to students and Phd students, are Anton Frisk Kockum (Chalmers) and Evert van Nieuwenburg (University of Leiden). The second project is focused on classifying topological phases in condensed matter physics using deep learning. Here we're currently looking at using and developing a new type of neural network architecture, equivariant NN, that build in symmetries of physical problems. Collaborators are Daniel Persson (Chalmers), Jan Gerken (TChalmers), Oleksandr Balabanov (external) and Hampus Linander (Chalmers) Relevant publications are: Data-driven decoding of quantum error correcting codes using graph neural networks The XYZ hexagonal stabilizer code M. Lange et al. arXiv2307.01241 Optimizing Variational Quantum Algorithms with qBang: Efficiently Interweaving Metric and Momentum to Tackle Flat Energy Landscapes D Fitzek et al. arXiv:2304.13882 B Srivastava, AF Kockum, M Granath Quantum 6, 698 (2022) Error-rate-agnostic decoding of topological stabilizer codes K Hammar, A Orekhov, PW Hybelius, AK Wisakanto, B Srivastava, Anton Frisk Kockum, Mats Granath Physical Review A 105 (4), 042616 (2022) Unsupervised interpretable learning of topological indices invariant under permutations of atomic bands, O Balabanov, M Granath, arXiv preprint arXiv:2008.07268 (2020) Deep Q-learning decoder for depolarizing noise on the toric code D Fitzek, M Eliasson, AF Kockum, M Granath Physical Review Research 2 (2), 023230 (2020) Unsupervised learning using topological data augmentation O Balabanov, M Granath Physical Review Research 2 (1), 013354 (2020) Quantum error correction for the toric code using deep reinforcement learning P Andreasson, J Johansson, S Liljestrand, M Granath Quantum 3, 183 (2019)