3篇
您的检索式：作者名="TENG Junhua"
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| 1 | Characterizing noise correlation and enhancing coherence via qubit motion显示文摘The identification of spacial noise correlation is of critical importance in developing error-corrected quantum devices,but it has barely been studied so far.In this work,we utilize an effective method called qubit motion,to efficiently determine the noise correlations between any pair of qubits in a 7-qubit superconducting quantum system.The noise correlations between the same pairs of qubits are also investigated when the qubits are at distinct operating frequencies.What’s more,in this multi-qubit system with the presence of noise correlations,we demonstrate the enhancing effect of qubit motion on the coherence of logic qubits,and we propose a Motion-CPMG operation sequence to more efficiently protect the logic state from decoherence,which is experimentally demonstrated to extend the coherence time of logic qubits by nearly one order of magnitude. | Jiaxiu Han Zhiyuan Li Jingning Zhang Huikai Xu Kehuan Linghu Yongchao Li Chengyao Li Mo Chen Zhen Yang Junhua Wang Teng Ma Guangming Xue Yirong Jin Haifeng Yu | 2021 | Fundamental Research2021，1，1： | 1 |
| 2 | Learning robust features by extended generative stochastic networks显示文摘Deep neural networks have achieved state-of-the-art performance on many object recognition tasks,but they are vulnerable to small adversarial perturbations.In this paper,several extensions of generative stochastic networks(GSNs)are proposed to improve the robustness of neural networks to random noise and adversarial perturbations.Experimental results show that compared to normal GSN method,the extensions using adversarial examples,lateral connections and feedforward networks can improve the performance of GSNs by making the models more resistant to overfitting and noise. | Da Teng Xiao Song Guanghong Gong Junhua Zhou | 2018 | International Journal of Modeling, Simulation, and Scientific Computing2018，9，1： | 0 |
| 3 | Parallelism and Optimization of Numerical Ocean Forecasting Model显示文摘According to the characteristics of Chinese marginal seas, the Marginal Sea Model of China(MSMC) has been developed independently in China. Because the model requires long simulation time, as a routine forecasting model, the parallelism of MSMC becomes necessary to be introduced to improve the performance of it. However, some methods used in MSMC, such as Successive Over Relaxation(SOR) algorithm, are not suitable for parallelism. In this paper, methods are developedto solve the parallel problem of the SOR algorithm following the steps as below. First, based on a 3D computing grid system, an automatic data partition method is implemented to dynamically divide the computing grid according to computing resources. Next, based on the characteristics of the numerical forecasting model, a parallel method is designed to solve the parallel problem of the SOR algorithm. Lastly, a communication optimization method is provided to avoid the cost of communication. In the communication optimization method, the non-blocking communication of Message Passing Interface(MPI) is used to implement the parallelism of MSMC with complex physical equations, and the process of communication is overlapped with the computations for improving the performance of parallel MSMC. The experiments show that the parallel MSMC runs 97.2 times faster than the serial MSMC, and root mean square error between the parallel MSMC and the serial MSMC is less than 0.01 for a 30-day simulation(172800 time steps), which meets the requirements of timeliness and accuracy for numerical ocean forecasting products. | XU Jianliang PANG Renbo TENG Junhua LIANG Hongtao YANG Dandan | 2016 | Journal of Ocean University of China2016，15，5： | 0 |
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