Quantum Information Protection Scheme Based on Reinforcement Learning for Periodic Surface Codes

Xue, Yun-Jia; Wang, Haowen; Tian, Yu‐Ping; Wang, Yinuo; Wang, Yu-Xuan; Wang, Shumei

doi:10.1155/2022/7643871

“…However, due to the complexity of the combination of syndrome information, it is difficult for us to select them with commonly used statistical methods. Therefore, we now propose a machine-learning-based CNN decoder [33,34] where we exploit the translation invariance of the heavy hexagonal code to feed error syndromes to a convolutional neural network for a faster determination of optimal syndromes and achieve the maximum good threshold by optimizing the condition [35]. In this paper, we focus on the degree of local correlation of error syndromes depending on the distance of the heavy hexagonal code [36].…”

Section: Error Correctionmentioning

confidence: 99%

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

Li,

Gan

et al. 2023

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Topological quantum error-correcting codes are an important tool for realizing fault-tolerant quantum computers. Heavy hexagonal coding is a new class of quantum error-correcting coding that assigns physical and auxiliary qubits to the vertices and edges of a low-degree graph. The layout of heavy hexagonal codes is particularly suitable for superconducting qubit architectures to reduce frequency conflicts and crosstalk. Although various topological code decoders have been proposed, constructing the optimal decoder remains challenging. Machine learning is an effective decoding scheme for topological codes, and in this paper, we propose a machine learning heavy hexagonal decoder based on a convolutional neural network (CNN) to obtain the decoding threshold. We test our method on heavy hexagonal codes with code distance of three, five, and seven, and increase it to five, seven, and nine by optimizing the RestNet network architecture. Our results show that the decoder thresholding accuracies are about 0.57% and 0.65%, respectively, which are about 25% higher than the conventional decoding scheme under the depolarizing noise model. The proposed decoding architecture is also applicable to other topological code families.

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“…The surface code is a plane mapping of the toric code 26,27 , and it is also a good stabilizer code due to its topology. The surface code is defined on a 2D square lattice, auxiliary qubits 28,29 can be attached to the qubits we want to protect, and the measurement of syndromes can be performed, and the intersection of syndromes can help identify the most likely error operators. For the chosen lattice, the qubits are clearly indicated in Fig.…”

Section: Surface Codementioning

confidence: 99%

Multidimensional Bose quantum error correction based on neural network decoder

Wang

¹

,

Xue

²

,

Qu

³

et al. 2022

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Boson quantum error correction is an important means to realize quantum error correction information processing. In this paper, we consider the connection of a single-mode Gottesman-Kitaev-Preskill (GKP) code with a two-dimensional (2D) surface (surface-GKP code) on a triangular quadrilateral lattice. On the one hand, we use a Steane-type scheme with maximum likelihood estimation for surface-GKP code error correction. On the other hand, the minimum-weight perfect matching (MWPM) algorithm is used to decode surface-GKP codes. In the case where only the data GKP qubits are noisy, the threshold reaches σ ≈ 0.5 ($$\bar{p}\approx 12.3 \%$$ p ¯ ≈ 12.3 % ). If the measurement is also noisy, the threshold is reached σ ≈ 0.25 ($$\bar{p}\approx 10.02 \%$$ p ¯ ≈ 10.02 % ). More importantly, we introduce a neural network decoder. When the measurements in GKP error correction are noise-free, the threshold reaches σ ≈ 0.78 ($$\bar{p}\approx 15.12 \%$$ p ¯ ≈ 15.12 % ). The threshold reaches σ ≈ 0.34 ($$\bar{p}\approx 11.37 \%$$ p ¯ ≈ 11.37 % ) when all measurements are noisy. Through the above optimization method, multi-party quantum error correction will achieve a better guarantee effect in fault-tolerant quantum computing.

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“…The surface code is a topological stabilizer code. [39,40] The Pauli group is defined by the following formula:…”

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

0

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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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Quantum Information Protection Scheme Based on Reinforcement Learning for Periodic Surface Codes

Cited by 11 publications

References 41 publications

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

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

Multidimensional Bose quantum error correction based on neural network decoder

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

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