Zobrazeno 1 - 10
of 15 034
pro vyhledávání: '"WANG, TIAN"'
Autor:
Wang, Tian, Wang, Chuang
Pretraining methods gain increasing attraction recently for solving PDEs with neural operators. It alleviates the data scarcity problem encountered by neural operator learning when solving single PDE via training on large-scale datasets consisting of
Externí odkaz:
http://arxiv.org/abs/2410.20100
We performed a suit of three-dimensional hydrodynamical simulations with a resolution of $\sim10$ parsecs to investigate the development of multiphase galactic wind in M82. The star formation and related feedback processes are solved self-consistentl
Externí odkaz:
http://arxiv.org/abs/2410.09782
The cyclicity and Koblitz conjectures ask about the distribution of primes of cyclic and prime-order reduction, respectively, for elliptic curves over $\mathbb{Q}$. In 1976, Serre gave a conditional proof of the cyclicity conjecture, but the Koblitz
Externí odkaz:
http://arxiv.org/abs/2408.16641
Quantum private query (QPQ) is the quantum version for symmetrically private retrieval. However, the user privacy in QPQ is generally guarded in the non-realtime and cheat sensitive way. That is, the dishonest database holder's cheating to elicit use
Externí odkaz:
http://arxiv.org/abs/2407.19147
Identifying causal relations is crucial for a variety of downstream tasks. In additional to observational data, background knowledge (BK), which could be attained from human expertise or experiments, is usually introduced for uncovering causal relati
Externí odkaz:
http://arxiv.org/abs/2407.15259
Autor:
Wang, Tian-Yu, Wang, Dong
Publikováno v:
Physics Letters B 855, 138876 (2024)
The uncertainty principle is deemed as one of cornerstones in quantum mechanics, and exploring its lower limit of uncertainty will be helpful to understand the principle's nature. In this study, we propose a generalized entropic uncertainty relation
Externí odkaz:
http://arxiv.org/abs/2407.13104
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:
Wang, Tian, Wang, Chuang
Neural operators effectively solve PDE problems from data without knowing the explicit equations, which learn the map from the input sequences of observed samples to the predicted values. Most existing works build the model in the original geometric
Externí odkaz:
http://arxiv.org/abs/2406.03923
Aiming at privacy preservation, Federated Learning (FL) is an emerging machine learning approach enabling model training on decentralized devices or data sources. The learning mechanism of FL relies on aggregating parameter updates from individual cl
Externí odkaz:
http://arxiv.org/abs/2405.11758