Artificial Neural Network Syndrome Decoding on IBM Quantum Processors

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.

7 pages, 3 figures

References in corpus (12)

• Surface codes: Towards practical large-scale quantum computation
• Suppressing quantum errors by scaling a surface code logical qubit
• Mitigating measurement errors in multi-qubit experiments
• Low-distance Surface Codes under Realistic Quantum Noise
• Assembly and coherent control of a register of nuclear spin qubits
• Matching and maximum likelihood decoding of a multi-round subsystem quantum error correction experiment
• Calibrated decoders for experimental quantum error correction
• Neural-Network Decoders for Quantum Error Correction using Surface Codes:A Space Exploration of the Hardware Cost-Performance Trade-Offs
• Parallel window decoding enables scalable fault tolerant quantum computation
• Real-Time Decoding for Fault-Tolerant Quantum Computing: Progress, Challenges and Outlook
• A scalable and fast artificial neural network syndrome decoder for surface codes
• Techniques for combining fast local decoders with global decoders under circuit-level noise

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