Near Term Applications of Quantum Machine Learning Techniques to Quantum Chemistry

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励，该项目将量子计算和机器学习结合起来，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问题，如电子结构计算。近年来，量子计算受到了广泛的关注和广泛的研究，因为它有潜力通过利用叠加和纠缠等量子现象提供的独特优势来超越最好的经典计算技术。与此同时，机器学习作为人工智能最重要的子领域之一已经取得了显著的成功，在科学和工程领域有许多当前和未来的应用。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。凯斯教授积极参与针对本科和研究生化学学生的量子计算和量子信息的教育活动。该项目有助于在这一至关重要的领域培训未来的劳动力。该项目旨在发展量子机器学习——经典机器学习和量子计算的杂交——以解决三个主要的化学问题：1。利用受限玻尔兹曼机获得精确分子势能面的混合量子算法。2. 应用于寻找分子基态能量的量子神经网络模型实现非线性激活函数的方法。3. 基于纠缠的量子态分类的量子机器学习算法。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.