Near Term Applications of Quantum Machine Learning Techniques to Quantum Chemistry

量子机器学习技术在量子化学中的近期应用

• 批准号: 1955907
• 负责人: Sabre Kais
• 金额: $ 45万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1955907&HistoricalAwards=false
• 关键词: Near; Term; Applications; Quantum; Machine

Sabre Kais of Purdue University is supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry for a project that combines quantum computation and machine learning to develop quantum machine learning techniques. These techniques are aimed at grand challenge problems in chemistry such as electronic structure calculations. Quantum computation has received enormous attention and extensive investigation in recent years due to its potential to outperform the best classical computation techniques by exploiting the unique advantages provided by quantum phenomena such as superposition and entanglement. In the meanwhile, machine learning has demonstrated remarkable success as one of the most important subfields of artificial intelligence, with numerous current and prospective applications in science and engineering. The project is expected to generate both fundamental knowledge and practical applications in quantum algorithm designs, machine learning methods, molecular models, reaction simulations, and material designs. Professor Kais is active in educational activities focused on quantum computing and quantum information aimed at undergraduate and graduate chemistry students. The project helps to train the workforce of the future in this critically important area. The project aims to develop quantum machine learning – a hybridization of classical machine learning and quantum computation – to solve three major chemistry problems: 1. A hybrid quantum algorithm employing a restricted Boltzmann machine to obtain accurate molecular potential energy surfaces. 2. A way to realize non-linear activation functions in a quantum neural network model applied to finding the ground state energy for molecules. 3. A quantum machine learning algorithm to classify quantum states based on entanglement.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

普渡大学的Sabre Kais获得了化学系化学理论，模型和计算方法项目的奖项，该项目结合了量子计算和机器学习来开发量子机器学习技术。这些技术旨在解决化学中的重大挑战性问题，如电子结构计算。 近年来，量子计算受到了极大的关注和广泛的研究，因为它有可能通过利用量子现象（如叠加和纠缠）提供的独特优势来超越最好的经典计算技术。与此同时，作为人工智能最重要的子领域之一，机器学习已经取得了显着的成功，在科学和工程领域有许多当前和潜在的应用。 该项目预计将在量子算法设计，机器学习方法，分子模型，反应模拟和材料设计方面产生基础知识和实际应用。 Kais教授积极参与针对本科生和研究生化学学生的量子计算和量子信息教育活动。该项目有助于在这一至关重要的领域培训未来的劳动力。该项目旨在开发量子机器学习-经典机器学习和量子计算的混合-以解决三个主要的化学问题：1。一种利用限制玻尔兹曼机获得精确分子势能面的混合量子算法。2.一种在量子神经网络模型中实现非线性激活函数的方法，用于寻找分子的基态能量。3.基于纠缠对量子态进行分类的量子机器学习算法。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Statistical Correlation Between Quantum Entanglement and Spin–Orbit Coupling in Crossed Beam Molecular Dynamics

• DOI: 10.1002/qute.202100098
• 发表时间: 2021-07
• 期刊: Advanced Quantum Technologies
• 影响因子: 4.4
• 作者: Junxu Li;M. Sajjan;Sumit Suresh Kale;S. Kais

Quantum cluster algorithm for data classification

• DOI: 10.1186/s41313-021-00029-1
• 发表时间: 2021-06
• 期刊: Materials Theory
• 作者: Junxu Li;S. Kais

Quantum computing for atomic and molecular resonances

• DOI: 10.1063/5.0040477
• 发表时间: 2021-05-21
• 期刊: JOURNAL OF CHEMICAL PHYSICS
• 影响因子: 4.4
• 作者: Bian, Teng;Kais, Sabre
• 通讯作者: Kais, Sabre

Relaxation of stationary states on a quantum computer yields a unique spectroscopic fingerprint of the computer’s noise

量子计算机上稳态的弛豫产生了计算机噪声的独特光谱指纹

• DOI: 10.1038/s42005-022-00803-8
• 发表时间: 2022
• 期刊: Communications Physics
• 影响因子: 5.5
• 作者: Smart, Scott E.;Hu, Zixuan;Kais, Sabre;Mazziotti, David A.
• 通讯作者: Mazziotti, David A.

Variational Quantum Circuits to Prepare Low Energy Symmetry States

• DOI: 10.3390/sym14030457
• 发表时间: 2021-12
• 期刊: Symmetry
• 作者: R. Selvarajan;M. Sajjan;S. Kais