Zobrazeno 1 - 10
of 2 265
pro vyhledávání: '"Tang, Ho"'
Autor:
Tang, Ho Lun
Quantum Computation has attracted massive interest because of the recent technological advancement in both hardware and software suggesting the potential of quantum advantage. On the software side, hybrid classical-quantum algorithms are extensively
Externí odkaz:
https://hdl.handle.net/10919/119509
In the context of Monte Carlo sampling for lattice models, the complexity of the energy landscape often leads to Markov chains being trapped in local optima, thereby increasing the correlation between samples and reducing sampling efficiency. This st
Externí odkaz:
http://arxiv.org/abs/2410.04766
Autor:
Du, Guodong, Lee, Junlin, Li, Jing, Jiang, Runhua, Guo, Yifei, Yu, Shuyang, Liu, Hanting, Goh, Sim Kuan, Tang, Ho-Kin, He, Daojing, Zhang, Min
While fine-tuning pretrained models has become common practice, these models often underperform outside their specific domains. Recently developed model merging techniques enable the direct integration of multiple models, each fine-tuned for distinct
Externí odkaz:
http://arxiv.org/abs/2410.02396
We conduct a comprehensive \textit{ab initio} investigation of electron-electron interactions within the pyrochlore structures of R$_2$Ru$_2$O$_7$, R$_2$Ir$_2$O$_7$, Ca$_2$Ru$_2$O$_7$, and Cd$_2$Ru$_2$O$_7$, where R denotes a rare-earth element. Util
Externí odkaz:
http://arxiv.org/abs/2409.01123
Drawing inspiration from the philosophy of Yi Jing, the Yin-Yang pair optimization (YYPO) algorithm has been shown to achieve competitive performance in single objective optimizations, in addition to the advantage of low time complexity when compared
Externí odkaz:
http://arxiv.org/abs/2408.05564
Autor:
Du, Guodong, Jiang, Runhua, Yang, Senqiao, Li, Haoyang, Chen, Wei, Li, Keren, Goh, Sim Kuan, Tang, Ho-Kin
Darwinian evolution of the biological brain is documented through multiple lines of evidence, although the modes of evolutionary changes remain unclear. Drawing inspiration from the evolved neural systems (e.g., visual cortex), deep learning models h
Externí odkaz:
http://arxiv.org/abs/2408.05563
Neural architecture search (NAS) automates neural network design by using optimization algorithms to navigate architecture spaces, reducing the burden of manual architecture design. While NAS has achieved success, applying it to emerging domains, suc
Externí odkaz:
http://arxiv.org/abs/2408.05556
The two-dimensional (2D) Hubbard model is widely believed to contain the key ingredients of high-temperature superconductivity in cuprate materials. Here, we report a constrained path quantum Monte Carlo (CPQMC) study of the square-lattice extended H
Externí odkaz:
http://arxiv.org/abs/2408.01113
Autor:
Du, Guodong, Li, Jing, Liu, Hanting, Jiang, Runhua, Yu, Shuyang, Guo, Yifei, Goh, Sim Kuan, Tang, Ho-Kin
Fine-tuning pre-trained language models, particularly large language models, demands extensive computing resources and can result in varying performance outcomes across different domains and datasets. This paper examines the approach of integrating m
Externí odkaz:
http://arxiv.org/abs/2406.12208
Superconductivity arises when electrons form Cooper pairs with phase coherence. In contrast, a lack of phase coherence in Cooper pairs can lead to an uncondensed metallic ground state known as the Bose metal state. In this study, we investigate an at
Externí odkaz:
http://arxiv.org/abs/2406.08131