Target-generating quantum error correction coding scheme based on generative confrontation network

Wang, Haowen; Song, Zhaoyang; Wang, Yinuo; Tian, Yu‐Ping; Ma, Hongyang

doi:10.1007/s11128-022-03616-4

Cited by 12 publications

(11 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[46,47] The primary objective in decoding idle qubits in quantum computing is to effectively suppress logical error rates by applying error correction schemes when the physical error rate of qubits is lower than a certain threshold, which is a crucial measure of fault-tolerant performance. This article introduces and compares two different error noise models for decoding, namely the minimum weight perfect matching (MWPM) decoder [27,42] and the RL decoder. [38,[48][49][50]…”

Section: Decoding Strategymentioning

confidence: 99%

“…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%

See 1 more Smart Citation

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

Chen

Wang

et al. 2023

Chinese Phys. B

View full text Add to dashboard Cite

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.

show abstract

Section: Decoding Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Chen

Wang

et al. 2023

Chinese Phys. B

View full text Add to dashboard Cite

show abstract

“…On the other hand, we envision related explorations such as applying the NFT to analyze the more complicated interferometry setups [23,27], or considering the influence of random noise on the output data. Using the machine-learning networks for computing the eigenvalue spectra [42] would be another interesting prospect, while this type of technics has been successfully used in the quantum error correction and proved to be efficient to some extent [43,44]. Frontiers in Physics frontiersin.org…”

Section: Summariesmentioning

confidence: 99%

Nonlinear Fourier analysis of matter-wave soliton interferometry

Feng

Sun²,

Yu³

2023

Front. Phys.

View full text Add to dashboard Cite

The bright solitons in quasi-1D atomic Bose-Einstein condensates are good candidates for constructing matter-wave interferometers with high sensitivity and long phase-accumulation times. Such interferometers at the mean-field level can be theoretically studied within the framework of quasi-1D Gross-Pitaevskii (GP) equation with narrow repulsive potential barriers. In this paper we present a basic proposal of using the nonlinear Fourier transform (NFT), also known as the inverse scattering transform, as an effective tool to analyze the soliton contents for those interferometers, which thanks to the nearly integrable nature of the GP equation when the normalized atom number fraction near the barrier is small. Based on typical cases, we show that the soliton components can be accurately detected from the output wave fields of the interferometers by computing the NFT spectra.

show abstract

“…These results show that the surface-GKP code is comparable to the toric-GKP code under the same noise model. The neural network decoder is fast enough to decode in topological quantum error correction codes 20 . It achieves an exponential improvement, and solves the hardware overhead 21 problem by using the algorithmic improvement.…”

Section: Introductionmentioning

confidence: 99%

Multidimensional Bose quantum error correction based on neural network decoder

Wang

Xue

et al. 2022

npj Quantum Inf

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Target-generating quantum error correction coding scheme based on generative confrontation network

Cited by 12 publications

References 47 publications

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

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

Nonlinear Fourier analysis of matter-wave soliton interferometry

Multidimensional Bose quantum error correction based on neural network decoder

Contact Info

Product

Resources

About