Zobrazeno 1 - 10
of 113
pro vyhledávání: '"Lee, Chee-Kong"'
Designing molecules with desirable physiochemical properties and functionalities is a long-standing challenge in chemistry, material science, and drug discovery. Recently, machine learning-based generative models have emerged as promising approaches
Externí odkaz:
http://arxiv.org/abs/2304.12436
The Transformer architecture has achieved remarkable success in a number of domains including natural language processing and computer vision. However, when it comes to graph-structured data, transformers have not achieved competitive performance, es
Externí odkaz:
http://arxiv.org/abs/2210.03930
Many data mining tasks rely on graphs to model relational structures among individuals (nodes). Since relational data are often sensitive, there is an urgent need to evaluate the privacy risks in graph data. One famous privacy attack against data ana
Externí odkaz:
http://arxiv.org/abs/2209.07807
Autor:
Najafabadi, Mojdeh S., Schumayer, Daniel, Lee, Chee Kong, Jaksch, Dieter, Hutchinson, David A. W.
A large class of optimisation problems can be mapped to the Ising model where all details are encoded in the coupling of spins. The task of the original mathematical optimisation is then equivalent to finding the ground state of the corresponding spi
Externí odkaz:
http://arxiv.org/abs/2208.05270
The recent advancement of quantum computer hardware offers the potential to simulate quantum many-body systems beyond the capability of its classical counterparts. However, most current works focus on simulating the ground-state properties or pure-st
Externí odkaz:
http://arxiv.org/abs/2206.05571
Autor:
Zhang, Shi-Xin, Allcock, Jonathan, Wan, Zhou-Quan, Liu, Shuo, Sun, Jiace, Yu, Hao, Yang, Xing-Han, Qiu, Jiezhong, Ye, Zhaofeng, Chen, Yu-Qin, Lee, Chee-Kong, Zheng, Yi-Cong, Jian, Shao-Kai, Yao, Hong, Hsieh, Chang-Yu, Zhang, Shengyu
Publikováno v:
Quantum 7, 912 (2023)
TensorCircuit is an open source quantum circuit simulator based on tensor network contraction, designed for speed, flexibility and code efficiency. Written purely in Python, and built on top of industry-standard machine learning frameworks, TensorCir
Externí odkaz:
http://arxiv.org/abs/2205.10091
Autor:
Zhang, Hui, Lau, Jonathan Wei Zhong, Wan, Lingxiao, Shi, Liang, Cai, Hong, Luo, Xianshu, Lo, Patrick, Lee, Chee-Kong, Kwek, Leong-Chuan, Liu, Ai Qun
Machine learning methods have revolutionized the discovery process of new molecules and materials. However, the intensive training process of neural networks for molecules with ever-increasing complexity has resulted in exponential growth in computat
Externí odkaz:
http://arxiv.org/abs/2203.02285
The search for new high-performance organic semiconducting molecules is challenging due to the vastness of the chemical space, machine learning methods, particularly deep learning models like graph neural networks (GNNs), have shown promising potenti
Externí odkaz:
http://arxiv.org/abs/2112.01633
Classical simulation of real-space quantum dynamics is challenging due to the exponential scaling of computational cost with system dimensions. Quantum computer offers the potential to simulate quantum dynamics with polynomial complexity; however, ex
Externí odkaz:
http://arxiv.org/abs/2110.06143
Predicting molecular properties with data-driven methods has drawn much attention in recent years. Particularly, Graph Neural Networks (GNNs) have demonstrated remarkable success in various molecular generation and prediction tasks. In cases where la
Externí odkaz:
http://arxiv.org/abs/2110.00987