Convolutional-Neural-Network-Based Hexagonal Quantum Error Correction Decoder

Li, Aoqing; Li, Fan; Gan, Qidi; Ma, Hongyang

doi:10.3390/app13179689

Cited by 4 publications

(1 citation statement)

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“…Among them, various neural networks have different decoding methods, and various neural networks have also been proposed, [27][28][29][30] such as feedforward neural networks (FFNN), recurrent neural networks (RNN), and convolutional neural networks (CNN). Decoding based on neural network [31][32][33] shortens the time required for decoding and can achieve an effect similar to that of classical decoding algorithms. However, as the code distance increases, the number of samples required for neural network training increases exponentially.…”

Section: Introductionmentioning

confidence: 99%

Recurrent neural network decoding of rotated surface codes based on distributed strategy

Li 李,

Gan 甘

et al. 2024

Chinese Phys. B

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Quantum error correction is a crucial technology for realizing quantum computers. These computers achieve fault-tolerant quantum computing by detecting and correcting errors using decoding algorithms. Quantum error correction using neural network-based machine learning methods is a promising approach that is adapted to physical systems without the need to build noise models. In this paper, we use a distributed decoding strategy, which effectively alleviates the problem of exponential growth of the training set required for neural networks as the code distance of quantum error-correcting codes increases. Our decoding algorithm is based on renormalization group decoding and recurrent neural network decoder. The recurrent neural network is trained through the Resnet architecture to improve its decoding accuracy. Then we tested the decoding performance of our distributed strategy decoder, recurrent neural network decoder and the classic minimum weight perfect matching(MWPM) decoder for rotated surface codes with different code distances under the circuit noise model, the thresholds of this three decoders are about 0.0052, 0.0051, and 0.0049 respectively. Our results demonstrate that the distributed strategy decoder outperforms the other two decoders, achieving approximately a 5% improvement in decoding efficiency compared to the MWPM decoder and approximately a 2% improvement compared to the recurrent neural network decoder.

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Section: Introductionmentioning

confidence: 99%

Recurrent neural network decoding of rotated surface codes based on distributed strategy

Li 李,

Gan 甘

et al. 2024

Chinese Phys. B

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Quantum error correction for heavy hexagonal code using deep reinforcement learning with policy reuse

Ji,

Chen,

Wang

et al. 2024

Quantum Inf Process

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Quantum error-correction using humming sparrow optimization based self-adaptive deep cnn noise correction module

Shinde,

Bandaru

2024

Sci Rep

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The error correction model’s main purpose in heavy hexagonal quantum codes is to improve their reliability for quantum computing applications. Existing challenges include finding the optimal decoder for quantum error correction in heavy hexagonal codes. This research propels the frontier of quantum error correction, with a specific focus on tailoring topological quantum error-correcting codes for the unique challenges posed by superconducting qubits in quantum computers. In response, this research harnesses the power of deep learning, presenting a Humming sparrow optimization based self-adaptive deep CNN (HSO-based SADCNN) model designed for heavy hexagonal codes. This decoder incorporates a Self-adaptive Deep CNN (SADCNN) Noise Correction Module, a sophisticated component to refine error correction. The proposed decoder’s efficacy is rigorously evaluated across varying code distances (three, five, and seven) using the Humming Sparrow Optimization (HSO) algorithm. HSO, intricately designed to fine-tune the SADCNN decoder, significantly enhances its error correction capabilities for heavy hexagonal quantum codes. The algorithm seamlessly integrates advantageous characteristics of herding and tracing from Humming Bird optimization and Sparrow search optimization, representing a critical stride in advancing the reliability of quantum computing applications, particularly within the intricate domain of heavy hexagonal quantum codes. Based upon the achievements, the Training Percentage (TP) 90 metrics demonstrate significant progress, boasting a commendable accuracy of $$97.35\%$$ 97.35 % , coupled with reduced logical error probability and a diminished bit error rate, marked at 5.51 and 3.72, respectively.

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Convolutional-Neural-Network-Based Hexagonal Quantum Error Correction Decoder

Cited by 4 publications

References 50 publications

Recurrent neural network decoding of rotated surface codes based on distributed strategy

Recurrent neural network decoding of rotated surface codes based on distributed strategy

Quantum error correction for heavy hexagonal code using deep reinforcement learning with policy reuse

Quantum error-correction using humming sparrow optimization based self-adaptive deep cnn noise correction module

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