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

• URL: http://arxiv.org/abs/2110.10896v1
• Date: Thu, 21 Oct 2021 04:54:44 GMT
• Title: Efficient Decoding of Surface Code Syndromes for Error Correction in Quantum Computing
• Authors: Debasmita Bhoumik, Pinaki Sen, Ritajit Majumdar, Susmita Sur-Kolay, Latesh Kumar K J, and Sundaraja Sitharama Iyengar
• Abstract summary: 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. Our results show that our proposed decoding method achieves $sim10 times$ and $sim2 times$ higher values of pseudo-threshold and threshold respectively. We show that usage of more sophisticated ML models with higher training/testing time, do not provide significant improvement in the decoder performance.
• Score: 0.09236074230806578
• License: http://creativecommons.org/licenses/by/4.0/
• 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 $\sim10 \times$ and $\sim2 \times$ 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 $40:60$.

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