Efficient Decoding of Surface Code Syndromes for Error Correction in Quantum Computing

Abstract

Errors in surface code have typically been decoded by Minimum Weight Perfect Matching (MWPM) based method. Recently, neural-network-based Machine Learning (ML) techniques have been employed for this purpose. Here we propose a two-level (low and high) ML-based decoding scheme, where the first level corrects errors on physical qubits and the second one corrects any existing logical errors, for different noise models. Our results show that our proposed decoding method achieves $\sim 10\times$ and $\sim 2\times$ higher values of pseudo-threshold and threshold respectively, than for MWPM. We show that usage of more sophisticated ML models with higher training/testing time, do not provide significant improvement in the decoder performance. Finally, data generation for training the ML decoder requires significant overhead hence lower volume of training data is desirable. We have shown that our decoder maintains a good performance with the train-test-ratio as low as $40:60$.

References in corpus (11)

• The theory of variational hybrid quantum-classical algorithms
• High threshold universal quantum computation on the surface code
• Quantum codes on a lattice with boundary
• Quantum computing with nearest neighbor interactions and error rates over 1%
• Towards practical classical processing for the surface code
• Asymmetric quantum error correcting codes
• Machine-learning-assisted correction of correlated qubit errors in a topological code
• Decoding Small Surface Codes with Feedforward Neural Networks
• Deep Neural Network Probabilistic Decoder for Stabilizer Codes
• High threshold error correction for the surface code
• Error tracing in linear and concatenated quantum circuits

Cited by in corpus (1)

• Surface Code Design for Asymmetric Error Channels