Quantum error correction is a crucial technology for realizing quantum computers.These computers achieve faulttolerant quantum computing by detecting and correcting errors using decoding algorithms.Quantum error corre...Quantum error correction is a crucial technology for realizing quantum computers.These computers achieve faulttolerant 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 test 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 these 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.展开更多
This paper proposes a 3D 2-node element for beams and cables. Main improvements of the element are two new interpolation functions for beam axis and cross-sectional rotation. New interpolation functions employ implici...This paper proposes a 3D 2-node element for beams and cables. Main improvements of the element are two new interpolation functions for beam axis and cross-sectional rotation. New interpolation functions employ implicit functions to simulate large deformations. In the translational interpolation function, two parameters which affect lateral deflection geometry are defined implicitly through nonlinear equations. The proposed translational interpolation function is shown to be more accurate than Hermitian function at large deformations. In the rotational interpolation function, twist rate is defined implicitly through a torsional continuity equation. Cross-sectional rotation which is strictly consistent to beam axis is obtained through separate bending rotation interpolation and torsional rotation interpolation. The element model fully accounts for geometric nonlinearities and coupling effects,and thus,can simulate cables with zero bending stiffness. Stiffness matrix and load vector have been derived using symbolic computation. Source code has been generated automatically.Numerical examples show that the proposed element has significantly higher accuracy than conventional 2-node beam elements under the same meshes for geometrically nonlinear problems.展开更多
基金Project supported by Natural Science Foundation of Shandong Province,China (Grant Nos.ZR2021MF049,ZR2022LLZ012,and ZR2021LLZ001)。
文摘Quantum error correction is a crucial technology for realizing quantum computers.These computers achieve faulttolerant 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 test 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 these 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.
基金Sponsored by the National Natural Science Foundation of China(Grant No.91215302)
文摘This paper proposes a 3D 2-node element for beams and cables. Main improvements of the element are two new interpolation functions for beam axis and cross-sectional rotation. New interpolation functions employ implicit functions to simulate large deformations. In the translational interpolation function, two parameters which affect lateral deflection geometry are defined implicitly through nonlinear equations. The proposed translational interpolation function is shown to be more accurate than Hermitian function at large deformations. In the rotational interpolation function, twist rate is defined implicitly through a torsional continuity equation. Cross-sectional rotation which is strictly consistent to beam axis is obtained through separate bending rotation interpolation and torsional rotation interpolation. The element model fully accounts for geometric nonlinearities and coupling effects,and thus,can simulate cables with zero bending stiffness. Stiffness matrix and load vector have been derived using symbolic computation. Source code has been generated automatically.Numerical examples show that the proposed element has significantly higher accuracy than conventional 2-node beam elements under the same meshes for geometrically nonlinear problems.
