Neural-Network Decoders for Quantum Error Correction Using Surface Codes
A Space Exploration of the Hardware Cost-Performance Tradeoffs
R.W.J. Overwater (QCD/Sebastiano Lab, TU Delft - QuTech Advanced Research Centre)
M. Babaie (TU Delft - QuTech Advanced Research Centre, TU Delft - Electronics)
Fabio Sebastiano (TU Delft - Quantum Circuit Architectures and Technology, TU Delft - QuTech Advanced Research Centre)
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Abstract
Quantum error correction (QEC) is required in quantum computers to mitigate the effect of errors on physical qubits. When adopting a QEC scheme based on surface codes, error decoding is the most computationally expensive task in the classical electronic back-end. Decoders employing neural networks (NN) are well-suited for this task but their hardware implementation has not been presented yet. This work presents a space exploration of fully connected feed-forward NN decoders for small distance surface codes. The goal is to optimize the NN for the high-decoding performance, while keeping a minimalistic hardware implementation. This is needed to meet the tight delay constraints of real-time surface code decoding. We demonstrate that hardware-based NN-decoders can achieve the high-decoding performance comparable to other state-of-the-art decoding algorithms whilst being well below the tight delay requirements (\approx 440\ ns) of current solid-state qubit technologies for both application-specific integrated circuit designs (< \!30\ ns) and field-programmable gate array implementations (<\! 90\ ns). These results indicate that NN-decoders are viable candidates for further exploration of an integrated hardware implementation in future large-scale quantum computers.
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