Strong electron correlations in quantum chemistry: new approaches from machine learning, quantum computing and time-dependent quantum control

量子化学中的强电子相关性：机器学习、量子计算和瞬态量子控制的新方法

• 批准号: RGPIN-2020-04306
• 负责人: DeBaerdemacker, Stijn
• 金额: $ 2.48万
• 依托单位: University of New Brunswick
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=739778
• 关键词: Strong; electron; correlations; quantum; chemistry

The ability to universally solve quantum many-body systems at high accuracy with low computational cost would have unprecedented consequences for human kind, allowing us to design and accurately compute sophisticated molecules with important applications in chemical, bio-medical, and material science on computer chips, rather than in expensive experimental laboratories. While we are not quite there yet, promising techniques are on the horizon to scale up current theoretical methods to more than a few atoms per molecule. In the past five years, the recently relocated quantum chemistry group at the University of New Brunswick (QuNB), and collaborators have proposed a computationally tractable Geminal theory: an electronic structure method for strongly correlated molecules based on the premises of Lewis electron pairs, rather than molecular orbitals. The Geminal project emerged from a fruitful cross fertilization between theoretical chemistry and mathematical/nuclear physics with ideas from integrability and the seniority scheme. Notwithstanding the strong quantum correlations encoded in the Geminal wavefunction ansatz, the method has a good computational scaling, so other research groups are currently investigating it for larger systems. While the mathematical formulation of Geminal theory is currently investigated in the framework of a Tier-2 Canada Research Chair, the present Discovery Grant proposal goes beyond traditional electronic structure methods by embracing newly emerged technologies with disrupting potential to the field. In three Work Packages (WP), we will explore, develop and establish WP1(GemQC): the power of Quantum Computing to design a fully correlated Geminal theory; WP2(ML-DMET): the connection between Machine Learning and Density Matrix Embedding Theory; WP3(e-FMD): Fermionic Molecular Dynamics as an intuitive classical picture of electron bonding and dynamics probed by attosecond laser fields. The proposed research program will provide excellent HQP training opportunities in both hard and soft skills for 3 PhD students, 2 MSc students and several undergraduate students. The hard skills that come with theoretical electronic structure theory are mathematical abstraction and numerical modeling, both skills that are in high demand in academia and industry. The embedding within a CRC research group will allow for high-quality training on important soft skills such as (international) collaboration and independence, with strong respect for Equity Diversity and Inclusion. Canada is a global leader in both the fundamentals and applications of Machine Learning and Quantum Computing, so this research program will enhance Canada's leading position, both by delivering new theoretical methods published in top-tier academic journals, open source (quantum) computer software packages that implement those methods, as the training of HQP in that area.

以低计算成本以高精度普遍解决量子多体系统的能力将对人类产生前所未有的影响，使我们能够在计算机芯片上设计和精确计算在化学，生物医学和材料科学中具有重要应用的复杂分子，而不是昂贵的实验室。虽然我们还没有完全达到目标，但有希望的技术即将出现，可以将当前的理论方法扩展到每个分子中的几个原子以上。 在过去的五年里，最近搬迁的量子化学小组在新玩法大学（QuNB），和合作者提出了一个计算上易于处理的Geminal理论：强相关分子的电子结构方法基于刘易斯电子对的前提，而不是分子轨道。Geminal项目产生于理论化学和数学/核物理之间富有成效的交叉施肥，其思想来自可积性和资历计划。尽管在Geminal波函数中编码了强量子相关，但该方法具有良好的计算缩放，因此其他研究小组目前正在研究更大的系统。 虽然Geminal理论的数学公式目前正在Tier-2 Canada Research Chair的框架内进行研究，但目前的发现补助金提案超越了传统的电子结构方法，采用了具有破坏潜力的新技术。在三个工作包（WP）中，我们将探索，开发和建立WP 1（GemQC）：量子计算设计完全相关的Geminal理论的力量; WP 2（ML-DMET）：机器学习和密度矩阵嵌入理论之间的联系; WP 3（e-FMD）：费米分子动力学作为阿秒激光场探测的电子键合和动力学的直观经典图像。 该研究项目将为3名博士生、2名硕士生和几名本科生提供优秀的HQP培训机会。理论电子结构理论带来的硬技能是数学抽象和数值建模，这两种技能在学术界和工业界都有很高的需求。嵌入CRC研究小组将允许在重要的软技能方面进行高质量的培训，例如（国际）合作和独立性，同时高度尊重公平、多样性和包容性。 加拿大是机器学习和量子计算的基础和应用的全球领导者，因此该研究计划将通过提供在顶级学术期刊上发表的新理论方法，实现这些方法的开源（量子）计算机软件包，作为HQP在该领域的培训，来提高加拿大的领先地位。