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Quantum error correction (QEC) is essential for quantum computing to mitigatethe effect of errors on qubits, and surface code (SC) is one of the mostpromising QEC methods. Decoding SCs is the most computational expensive task inthe control device of quantum computers (QCs), and many works focus on accuratedecoding algorithms for SCs, including ones with neural networks (NNs).Practical QCs also require low-latency decoding because slow decoding leads tothe accumulation of errors on qubits, resulting in logical failures. For QCswith superconducting qubits, a practical decoder must be very power-efficientin addition to having high accuracy and low latency. In order to reduce thehardware complexity of QC, we are supposed to decode SCs in a cryogenicenvironment with a limited power budget, where superconducting qubits operate.  In this paper, we propose an NN-based accurate, fast, and low-power decodercapable of decoding SCs and lattice surgery (LS) operations with measurementerrors on ancillary qubits. To achieve both accuracy and hardware efficiency ofthe SC decoder, we apply a binarized NN. We design a neural processing unit(NPU) for the decoder with SFQ-based digital circuits and evaluate it with aSPICE-level simulation. We evaluate the decoder performance by a quantum errorsimulator for the single logical qubit protection and the minimum operation ofLS with code distances up to 13, and it achieves 2.5% and 1.0% accuracythresholds, respectively.

NEO-QEC: Neural Network Enhanced Online Superconducting Decoder for Surface Codes