Print Email Facebook Twitter Neural-Network Decoders for Quantum Error Correction Using Surface Codes Title Neural-Network Decoders for Quantum Error Correction Using Surface Codes: A Space Exploration of the Hardware Cost-Performance Tradeoffs Author Overwater, R.W.J. (TU Delft QCD/Sebastiano Lab; TU Delft QuTech Advanced Research Centre) Babaie, M. (TU Delft Electronics; TU Delft QuTech Advanced Research Centre) Sebastiano, F. (TU Delft Quantum Circuit Architectures and Technology; TU Delft QuTech Advanced Research Centre) Date 2022 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. Subject QubitDecodingCodesHardwareQuantum computingLogic gatesArtificial neural networksApplication-specific integrated circuit (ASIC)complementary metal-oxide semiconductor (CMOS)CMOS integrated circuitscombinational circuitscryo-CMOS decodingcryogenic electronicsdigital integrated circuitserror correction codesfeedforward neural networks (NNs)field programmable gate array (FPGA)fixed-point arithmeticmachine learningNNspareto analysisquantum computingquantum-error-correction (QEC) codessupervised learningsurface codes (SCs) To reference this document use: http://resolver.tudelft.nl/uuid:d939d34e-8dbd-43ad-b100-655a2931c3bf DOI https://doi.org/10.1109/TQE.2022.3174017 ISSN 2689-1808 Source IEEE Transactions on Quantum Engineering, 3 Part of collection Institutional Repository Document type journal article Rights © 2022 R.W.J. Overwater, M. Babaie, F. Sebastiano Files PDF Neural_Network_Decoders_f ... deoffs.pdf 3.27 MB Close viewer /islandora/object/uuid:d939d34e-8dbd-43ad-b100-655a2931c3bf/datastream/OBJ/view