Quantum error correction techniques are important for implementing fault-tolerant quantum computation, and topological quantum error correcting codes provide feasibility for implementing large-scale fault-tolerant qua...
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Quantum error correction techniques are important for implementing fault-tolerant quantum computation, and topological quantum error correcting codes provide feasibility for implementing large-scale fault-tolerant quantum computation. Here, we propose a deep reinforcement learning framework for implementing quantum error correction algorithms for errors on heavy hexagonal codes. Specifically, we construct the double deep Q learning model with policy reuse method, so that the decoding agent does not have to explore the learning from scratch when dealing with new error syndrome, but instead reuses past policies, which can reduce the computational complexity. And the double deep Q network can avoid the problem of threshold being overestimated and get the true decoding threshold. Our experimental results show that the error correction accuracy of our decoder can reach 91.86%. Different thresholds are obtained according to the code distance, which is 0.0058 when the code distance is 3, 5, 7, and 0.0065 when the code distance is 5, 7, 9, both higher than that of the classical minimum weight perfect matching decoder. Compared to the threshold of the MWPM decoder under the depolarizing noise model, the threshold of our decoder is improved by 32.63%, which enables better fault-tolerant quantum computation.
heavyhexagonal coding is a type of quantum error-correcting coding in which the edges and vertices of a low-degree graph are assigned auxiliary and physical qubits. While many topological code decoders have been pres...
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heavyhexagonal coding is a type of quantum error-correcting coding in which the edges and vertices of a low-degree graph are assigned auxiliary and physical qubits. While many topological code decoders have been presented, it is still difficult to construct the optimal decoder due to leakage errors and qubit collision. Therefore, this research proposes a Re-locative Guided Search optimized self-sparse attention-enabled convolutional Neural Network with Long Short-Term Memory (RlGS2-DCNTM) for performing effective error correction in quantum codes. The integration of the self-sparse attention mechanism in the proposed model increases the feature learning ability of the model to selectively focus on informative regions of the input codes. In addition, the use of statistical features computes the statistical properties of the input, thus aiding the model to perform complex tasks effectively. For model tuning, this research utilizes the RIGS nature-inspired algorithm that mimics the re-locative, foraging, and hunting strategies, which avoids local optima problems and improves the convergence speed of the RlGS2-DCNTM for Quantum error correction. When compared with other methods, the proposed RlGS2-DCNTM algorithm offers superior efficacy with a Minimum Mean Squared Error (MSE) of 4.26, Root Mean Squared Error of 2.06, Mean Absolute Error of 1.14 and a maximum correlation and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R<^>2$$\end{document} of 0.96 and 0.92 respectively, which shows that the proposed model is highly suitable for real-time error decoding tasks.
Error syndromes for heavy hexagonal code and other topological codes such as surface code have typically been decoded by using Minimum Weight Perfect Matching- (MWPM) based methods. Recent advances have shown that top...
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Error syndromes for heavy hexagonal code and other topological codes such as surface code have typically been decoded by using Minimum Weight Perfect Matching- (MWPM) based methods. Recent advances have shown that topological codes can be efficiently decoded by deploying machine learning (ML) techniques, in particular with neural networks. In this work, we first propose an ML-based decoder for heavy hexagonal code and establish its efficiency in terms of the values of threshold and pseudo-threshold for various noise models. We show that the proposed ML-based decoding method achieves similar to 5x higher values of threshold than that for MWPM. Next, exploiting the property of subsystem codes, we define gauge equivalence for heavy hexagonal code, by which two distinct errors can belong to the same error class. A linear search-based method is proposed for determining the equivalent error classes. This provides a quadratic reduction in the number of error classes to be considered for both bit flip and phase flip errors and thus a further improvement of similar to 14% in the threshold over the basic ML decoder. Last, a novel technique based on rank to determine the equivalent error classes is presented, which is empirically faster than the one based on linear search.
Decoding error syndromes for topological quantum error correcting codes, such as surface and heavy hexagonal codes, is computationally expensive. While minimum weight perfect matching (MWPM) algorithms have been commo...
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ISBN:
(纸本)9798350354119
Decoding error syndromes for topological quantum error correcting codes, such as surface and heavy hexagonal codes, is computationally expensive. While minimum weight perfect matching (MWPM) algorithms have been commonly used for decoding, recent works have demonstrated the efficacy of machine learning (ML), particularly neural networks, in decoding syndromes for these codes. In this study, we introduce a ML-based decoder tailored to heavy hexagonal code to address asymmetric noise channels which reflect real-world scenario better than the depolarization model considered in previous works. Our proposed decoder shows similar to 5x and similar to 22x improvements in the threshold values for amplitude and amplitude-phase damping noise models respectively over MWPM methods. Our decoder is also robust to changes in asymmetry, with the threshold reducing by only similar to 3.6% for a 10x change in asymmetry.
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