Machine learning meets quantum mechanics in catalysisdoi:10.3389/frqst.2023.1232903Lewis, James P.Pengju RenXiaodong WenYongwang LiGuanhua ChenFrontiers in Quantum Science & Technology
Origins and Development of Quantum Computing 量子计算的发展历程和技术背景: 1.1 量子计算的起源 Origins of Quantum Computing ·量子力学的诞生(Birth of Quantum Mechanics):量子力学作为量子计算的理论基础,其发展为量子计算的诞生奠定了基础。 ·理论提出(Theoretical Proposals):1980年代,理查德·费曼(Richard Feynma...
My problem of interest is a “simple” Quantum Mechanics eigen-function partial differential equation for few particle systems utilizing a wave-function expansion with “correlated Gaussian basis functions”.It’s a relatively simple problem really, and we...
Artificial Intelligence and Machine Learning 量子计算在人工智能和机器学习领域具有潜力,通过量子算法提升学习速度和模型性能。 ·量子机器学习 Quantum Machine Learning 量子机器学习算法能够处理大规模数据集,提高模型训练的速度和准确性,推动人工智能的发展。 ·量子数据处理 Quantum Data Processing 量子计算能够加速数据...
近日,来自 南洋理工大学计算与数据科学学院(NTU)、新加坡国立大学量子技术中心(CQT)、鸿海研究院(Hon Hai Research Institute) 的研究团队发布了一篇专为 AI 研究者与开发者 设计的 QML 教程,论文地址:Quantum Machine Learning: A Hands-...
近日,来自 南洋理工大学计算与数据科学学院(NTU)、新加坡国立大学量子技术中心(CQT)、鸿海研究院(Hon Hai Research Institute) 的研究团队发布了一篇专为 AI 研究者与开发者 设计的 QML 教程,论文地址:Quantum Machine Learning: A Hands-on Tutorial for Machine Learning Practitioners and Researchers(https://arxiv...
R. Ramakrishnan and O. A. von Lilienfeld. Machine learning, quantum mechanics, and chemical compound space. arXiv preprint arXiv:1510.07512, 2015.Ramakrishnan, R. & von Lilienfeld, O. A. Machine learning, quantum mechanics, and chemical compound space. arXiv preprint arXiv:1510.07512 (2015)....
Atomistic simulation methods can be broadly categorized into two classes depending on the way the system is described: using quantum mechanical (QM) calculations based on the electronic structure or molecular mechanics (MM) methods with predefined functional forms. Due to their higher computational cost...
The use of quantum computing for machine learning is among the most exciting prospective applications of quantum technologies. However, machine learning tasks where data is provided can be considerably different than commonly studied computational tasks. In this work, we show that some problems that ar...
In this work, we develop a deep learning framework that provides an accurate ML model of molecular electronic structure via a direct representation of the electronic Hamiltonian in a local basis representation. The model provides a seamless interface between quantum mechanics and ML by predicting the...