Zobrazeno 1 - 10
of 3 560
pro vyhledávání: '"Yun, Jie"'
Autor:
Xue, Cheng, Xu, Xiao-Fan, Zhuang, Xi-Ning, Sun, Tai-Ping, Wang, Yun-Jie, Tan, Ming-Yang, Ye, Chuang-Chao, Liu, Huan-Yu, Wu, Yu-Chun, Chen, Zhao-Yun, Guo, Guo-Ping
Nonlinear partial differential equations (PDEs) are crucial for modeling complex fluid dynamics and are foundational to many computational fluid dynamics (CFD) applications. However, solving these nonlinear PDEs is challenging due to the vast computa
Externí odkaz:
http://arxiv.org/abs/2411.06759
Surgical automation holds immense potential to improve the outcome and accessibility of surgery. Recent studies use reinforcement learning to learn policies that automate different surgical tasks. However, these policies are developed independently a
Externí odkaz:
http://arxiv.org/abs/2409.15651
Quantum collision model provides a promising tool for investigating system-bath dynamics. Most of the studies on quantum collision models work in the resonant regime. In quantum dynamics, the off-resonant interaction often brings in exciting ffects.
Externí odkaz:
http://arxiv.org/abs/2409.12738
HiMA: Hierarchical Quantum Microarchitecture for Qubit-Scaling and Quantum Process-Level Parallelism
Autor:
Zhou, Qi, Mei, Zi-Hao, Shi, Han-Qing, Guo, Liang-Liang, Yang, Xiao-Yan, Wang, Yun-Jie, Xu, Xiao-Fan, Xue, Cheng, Kong, Wei-Cheng, Wang, Jun-Chao, Wu, Yu-Chun, Chen, Zhao-Yun, Guo, Guo-Ping
Quantum computing holds immense potential for addressing a myriad of intricate challenges, which is significantly amplified when scaled to thousands of qubits. However, a major challenge lies in developing an efficient and scalable quantum control sy
Externí odkaz:
http://arxiv.org/abs/2408.11311
Autor:
Zhang, Sheng, Duan, Peng, Wang, Yun-Jie, Wang, Tian-Le, Wang, Peng, Zhao, Ren-Ze, Yang, Xiao-Yan, Zhao, Ze-An, Guo, Liang-Liang, Chen, Yong, Zhang, Hai-Feng, Du, Lei, Tao, Hao-Ran, Li, Zhi-Fei, Wu, Yuan, Jia, Zhi-Long, Kong, Wei-Cheng, Chen, Zhao-Yun, Wu, Yu-Chun, Guo, Guo-Ping
In the NISQ era, achieving large-scale quantum computing demands compact circuits to mitigate decoherence and gate error accumulation. Quantum operations with diverse degrees of freedom hold promise for circuit compression, but conventional approache
Externí odkaz:
http://arxiv.org/abs/2407.06687
Autor:
Chen, Zhao-Yun, Ma, Teng-Yang, Ye, Chuang-Chao, Xu, Liang, Tan, Ming-Yang, Zhuang, Xi-Ning, Xu, Xiao-Fan, Wang, Yun-Jie, Sun, Tai-Ping, Chen, Yong, Du, Lei, Guo, Liang-Liang, Zhang, Hai-Feng, Tao, Hao-Ran, Wang, Tian-Le, Yang, Xiao-Yan, Zhao, Ze-An, Wang, Peng, Zhang, Sheng, Zhang, Chi, Zhao, Ren-Ze, Jia, Zhi-Long, Kong, Wei-Cheng, Dou, Meng-Han, Wang, Jun-Chao, Liu, Huan-Yu, Xue, Cheng, Zhang, Peng-Jun-Yi, Huang, Sheng-Hong, Duan, Peng, Wu, Yu-Chun, Guo, Guo-Ping
Quantum computational fluid dynamics (QCFD) offers a promising alternative to classical computational fluid dynamics (CFD) by leveraging quantum algorithms for higher efficiency. This paper introduces a comprehensive QCFD method, including an iterati
Externí odkaz:
http://arxiv.org/abs/2406.06063
Autor:
Zhuang, Xi-Ning, Chen, Zhao-Yun, Xue, Cheng, Xu, Xiao-Fan, Wang, Chao, Liu, Huan-Yu, Sun, Tai-Ping, Wang, Yun-Jie, Wu, Yu-Chun, Guo, Guo-Ping
Quantum machine learning has demonstrated significant potential in solving practical problems, particularly in statistics-focused areas such as data science and finance. However, challenges remain in preparing and learning statistical models on a qua
Externí odkaz:
http://arxiv.org/abs/2406.01335
Autor:
Liu, Huan-Yu, Lin, Xiaoshui, Chen, Zhao-Yun, Xue, Cheng, Sun, Tai-Ping, Li, Qing-Song, Zhuang, Xi-Ning, Wang, Yun-Jie, Wu, Yu-Chun, Gong, Ming, Guo, Guo-Ping
The rapid development of quantum computers has enabled demonstrations of quantum advantages on various tasks. However, real quantum systems are always dissipative due to their inevitable interaction with the environment, and the resulting non-unitary
Externí odkaz:
http://arxiv.org/abs/2405.20712
Autor:
Zhang, Jiaxuan, Chen, Zhao-Yun, Wang, Yun-Jie, Lu, Bin-Han, Zhang, Hai-Feng, Li, Jia-Ning, Duan, Peng, Wu, Yu-Chun, Guo, Guo-Ping
Fault-tolerant quantum computing (FTQC) is essential for achieving large-scale practical quantum computation. Implementing arbitrary FTQC requires the execution of a universal gate set on logical qubits, which is highly challenging. Particularly, in
Externí odkaz:
http://arxiv.org/abs/2405.09035
Autor:
Xue, Cheng, Chen, Zhao-Yun, Zhuang, Xi-Ning, Wang, Yun-Jie, Sun, Tai-Ping, Wang, Jun-Chao, Liu, Huan-Yu, Wu, Yu-Chun, Wang, Zi-Lei, Guo, Guo-Ping
The field of quantum deep learning presents significant opportunities for advancing computational capabilities, yet it faces a major obstacle in the form of the "information loss problem" due to the inherent limitations of the necessary quantum tomog
Externí odkaz:
http://arxiv.org/abs/2402.18940