Low-loss belief propagation decoder with Tanner graph in quantum error-correction codes

Yan, Dandan; Fan, Xiaolong; Chen, Zhenyu; Ma, Hongyang

doi:10.1088/1674-1056/ac11cf

Cited by 5 publications

(6 citation statements)

References 45 publications

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“…The test is made for very short data length of size (40 packets). The challenge of such length is required especially for its application in the field of smart cities and their wireless sensor networks [13]- [20]. Table 2 shows the result for the simulation test of our LDHS-LT code with parameters (𝑑 = 1, 2, 3 or 4) and (𝑆 = 10), compared with traditional RSD-LT code with parameters (𝑐 = 0.02, 𝑐 = 0.02, 𝛿 = 0.1).…”

Section: Resultsmentioning

confidence: 99%

A Simulink model for modified fountain codes

Abdulkhaleq

Salih²,

Hasan³

et al. 2023

TELKOMNIKA

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This paper introduces a Simulink model design for a modified fountain code. The code is a new version of the traditional Luby transform (LT) codes. The design constructs the blocks required for generation of the generator matrix of a limited-degree-hopping-segment Luby transform (LDHS-LT) codes. This code is especially designed for short length data files which have assigned a great interest for wireless sensor networks. It generates the degrees in a predetermined sequence but random generation and partitioned the data file in segments. The data packets selection has been made serialy according to the integer generated from both degree and segment generators. The code is tested using Monte Carlo simulation approach with the conventional code generation using robust soliton distribution (RSD) for degree generation, and the simulation results approve better performance with all testing parameter.

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Section: Resultsmentioning

confidence: 99%

A Simulink model for modified fountain codes

Abdulkhaleq

Salih²,

Hasan³

et al. 2023

TELKOMNIKA

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“…We use a duel network in the double-Q learning algorithm [38] to increase the number of ipped bits of the error correction chain and to reduce the error rate threshold [39]. e double-Q learning abbreviated as DDQN is the optimization of the Q learning algorithm; it can solve the bootstrap bias [40] that Q learning is di cult to solve, alleviate the overestimation problem caused by maximization, and better solve the problem of our optimization threshold which is too high. We use the fully connected network structure in the confrontation network to encode the syndrome of the surface code and use the DDQN algorithm to optimize the value of the action (bit-ip and phase-ip) and use the convolutional neural network decoder [41] to decode the output eigenvalues to gradually nd the correction chain that we want to restore.…”

Section: Double-q Learningmentioning

confidence: 99%

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

Xue

Wang

Tian

et al. 2022

Quantum Engineering

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Quantum information transfer is an information processing technology with high speed and high entanglement with the help of quantum mechanics principles. To solve the problem of quantum information getting easily lost during transmission, we choose topological quantum error correction codes as the best candidate codes to improve the fidelity of quantum information. The stability of topological error correction codes brings great convenience to error correction. The quantum error correction codes represented by surface codes have produced very good effects in the error correction mechanism. In order to solve the problem of strong spatial correlation and optimal decoding of surface codes, we introduced a reinforcement learning decoder that can effectively characterize the spatial correlation of error correction codes. At the same time, we use a double-layer convolutional neural network model in the confrontation network to find a better error correction chain, and the generation network can approach the best correction model, to ensure that the discriminant network corrects more nontrivial errors. To improve the efficiency of error correction, we introduced a double-Q algorithm and ResNet network to increase the error correction success rate and training speed of the surface code. Compared with the previous MWPM 0.005 decoder threshold, the success rate has slightly improved, which can reach up to 0.0068 decoder threshold. By using the residual neural network architecture, we saved one-third of the training time and increased the training accuracy to about 96.6%. Using a better training model, we have successfully increased the decoder threshold from 0.0068 to 0.0085, and the depolarized noise model being used does not require a priori basic noise, so that the error correction efficiency of the entire model has slightly improved. Finally, the fidelity of the quantum information has successfully improved from 0.2423 to 0.7423 by using the error correction protection schemes.

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“…Quantum error correction (QEC), [1][2][3][4] which has gained popularity in recent years, is now regarded as the procedure in quantum computing that requires the most time and resources. However, given the current strategies for quantum computing, QEC is an effective means of reliable quantum computing and storage as well as protecting quantum information from loss.…”

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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Low-loss belief propagation decoder with Tanner graph in quantum error-correction codes

Cited by 5 publications

References 45 publications

A Simulink model for modified fountain codes

A Simulink model for modified fountain codes

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

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

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