Near Term Applications of Quantum Machine Learning Techniques to Quantum Chemistry

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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.