Presentation av mastersarbete i fysik. Titeln på examensarbetet är "Decoding the surface code using graph neural networks".
Quantum error correction is essential to achieve fault-tolerant quantum computation in the presence of noisy qubits. Among the most promising approaches to quantum error correction is the surface code [1], thanks to a scalable two-dimensional architecture, only nearest-neighbor interactions and a high error threshold. Decoding the surface code, i.e. finding the most likely error chain given a syndrome measurement outcome is a computationally complex task that involves solving a challenging optimization problem. Traditional decoders rely on classical algorithms, which, especially for larger systems, can be slow and may not always converge to the optimal solution. This thesis presents a novel approach to decoding the surface code using graph neural networks. By mapping the syndrome measurements to a graph and performing graph classification, we find that the graph neural networks can predict the most likely error configuration with high accuracy. Our results show, that the GNN-based decoder outperforms the classic minimum weight perfect matching (MWPM) decoder in terms of accuracy and decoding speed. With a phenomenological noise model with depolarizing noise and perfect syndrome measurements, our networks beat MWPM up to code-size 21 across all relevant error rates. Furthermore, the GNN is capable of surpassing MWPM under circuit-level noise up to code size 7. We also show that training the network on repetition code data from a recent experiment [2] produces per-step error rates comparable to those achieved with a matching decoder specifically adapted to the error rates of the physical qubits. This indicates that graph neural network decoders are capable of learning the underlying error distribution on the qubits. Our findings advance the field of quantum error correction and provide a promising new direction for the development of efficient decoding algorithms. [1] A. G. Fowler, M. Mariantoni, J. M. Martinis, and A. N.
Basudha Srivastava
Mats Granath
Cleland, PHYSICAL REVIEW A, 10.1103/PhysRevA.86.032324 (2012) [2] Google Quantum AI, Nature, 10.1038/s41586-022-05434-1 (2022)
https://chalmers.zoom.us/j/7419214619
Password: 437880