Artificial Neural Network Syndrome Decoding on IBM Quantum Processors

• URL: http://arxiv.org/abs/2311.15146v1
• Date: Sun, 26 Nov 2023 00:14:22 GMT
• Title: Artificial Neural Network Syndrome Decoding on IBM Quantum Processors
• Authors: Brhyeton Hall, Spiro Gicev, Muhammad Usman
• Abstract summary: Syndrome decoding is an integral but computationally demanding step in the implementation of quantum error correction for fault-tolerant quantum computing. We show that Artificial Neural Network (ANN) decoding can efficiently decode syndrome measurement data from heavy-hexagonal code architecture. Our work confirms the applicability of ANN decoding methods of syndrome data retrieved from experimental devices and establishes machine learning as a promising pathway for quantum error correction when quantum devices with below threshold error rates become available in the near future.
• Score: 0.7232471205719458
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Syndrome decoding is an integral but computationally demanding step in the implementation of quantum error correction for fault-tolerant quantum computing. Here, we report the development and benchmarking of Artificial Neural Network (ANN) decoding on IBM Quantum Processors. We demonstrate that ANNs can efficiently decode syndrome measurement data from heavy-hexagonal code architecture and apply appropriate corrections to facilitate error protection. The current physical error rates of IBM devices are above the code's threshold and restrict the scope of our ANN decoder for logical error rate suppression. However, our work confirms the applicability of ANN decoding methods of syndrome data retrieved from experimental devices and establishes machine learning as a promising pathway for quantum error correction when quantum devices with below threshold error rates become available in the near future.

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