Multidimensional Bose quantum error correction based on neural network decoder

Wang, Haowen; Xue, Yun-Jia; Qu, Yingjie; Mu, Xiang Dong; Ma, Hongyang

doi:10.1038/s41534-022-00650-z

Cited by 14 publications

(8 citation statements)

References 52 publications

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“…Finally, we will explore the use of quantum alpha for decoding strategies applied to the surface-GKP code in the field of quantum error correction. Our team has previously investigated the error-correction effectiveness of GKP surface codes using maximum likelihood estimation with a Steane-type scheme and introduced neural networks as decoders for GKP surface codes, achieving a high-performance threshold of 0.34 in noisy environments [60]. Next, we will continue to explore the performance of this model as a decoder for more bosonic codes.…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence warm-start approach: optimizing the generalization capability of QAOA in complex energy landscapes

Zhao,

Cheng,

Wang

et al. 2024

New J. Phys.

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To address the issue of the Quantum Approximate Optimization Algorithm (QAOA) frequently encountering local minima and the cost of parameter optimization within complex non-convex optimization energy landscapes, we consider a warm-start method. This approach leverages the characteristics of transition states (TS) in the enhanced optimizer, specifically descending along unique negative curvature directions, to find smaller local minima. Our research results indicate that with the assistance of an enhanced pre-training structure of the AlphaZero AI model, the initialization generalization ability of the new optimizer is significantly enhanced across various test sets. We train on 2-SAT training sets with clause densities between α≅ 2.6 and α≅ 2.89, and transfer to more complex test sets. Additionally, the average residual energy density in transfer learning consistently remains below 0.01, even achieving a high transfer success probability of 98% in hard instances with α≅ 3.7. The search efficiency, pre-trained by ensemble learning, was significantly enhanced, while only requiring simple interpolation of a few transition points to transfer on the global optimal solutions at higher sample clause densities.

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Section: Discussionmentioning

confidence: 99%

Artificial intelligence warm-start approach: optimizing the generalization capability of QAOA in complex energy landscapes

Zhao,

Cheng,

Wang

et al. 2024

New J. Phys.

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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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“…If the decoding process takes longer duration than the budgeted error correction time, errors will accumulate, eventually reaching an uncorrectable error state. To address rapid decoding difficulties, machine learning techniques have been employed in various quantum physics domains, and different types of neural networks [26][27][28][29] have also been studied during this time.…”

Section: Introductionmentioning

confidence: 99%

Approximate error correction scheme for three-dimensional surface codes based reinforcement learning

Chen

Wang

et al. 2023

Chinese Phys. B

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Quantum error correction technology is an important method to eliminate errors during the operation of quantum computers. In order to solve the problem of the influence of errors on physical qubits, this paper proposes an approximate error correction scheme that performs dimension mapping operations on surface codes. This error correction scheme utilizes the topological properties of error correction codes to map the surface code dimension to three dimensions. Compared to previous error correction schemes, the three-dimensional surface code exhibits good scalability due to its higher redundancy and more efficient error correction capabilities. By reducing the number of ancilla qubits required for error correction, this approach achieves savings in measurement space and reduces resource consumption costs. In order to improve the decoding efficiency and solve the problem of the correlation between the surface code stabilizer and the 3D space after dimension mapping, we employ a Reinforcement Learning (RL) decoder based on Deep Qlearning, which enables faster identification of the optimal syndrome and achieves better thresholds through conditional optimization. Compared to the Minimum Weight Perfect Matching (MWPM) decoding, the threshold of the RL trained model reaches 0.78%, which is 56% higher and enables large-scale fault-tolerant quantum computation.

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Multidimensional Bose quantum error correction based on neural network decoder

Cited by 14 publications

References 52 publications

Artificial intelligence warm-start approach: optimizing the generalization capability of QAOA in complex energy landscapes

Artificial intelligence warm-start approach: optimizing the generalization capability of QAOA in complex energy landscapes

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

Approximate error correction scheme for three-dimensional surface codes based reinforcement learning

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