Quantum Machine Learning

Quantum machine learning — a hybridization of classical machine learning techniques with quantum computation – is emerging as a powerful approach both allowing speed-ups and improving classical machine learning algorithms. This program will leverage our expertise in developing quantum algorithms to fully realize the tremendous promise of combining quantum algorithms with machine learning to solve important and challenging problems in quantum chemistry. The first part of the project focuses on combining quantum computing with machine learning techniques for the intent of performing electronic structure calculations. Our initial results for small molecules indicate the feasibility of this combined approach. The second part of the project focuses on developing quantum machine learning techniques for quantum coherence and dynamics for controlling the outcome of chemical reactions. The final part will be on developing hybrid quantum classical machine learning algorithms for data classifications, particularly for quantum phases.

Relevant Publications:

Rongxin Xia and Sabre Kais, "Quantum Machine Learning for Electronic Structure Calculations," Nature Communications 9, 4195 (2018)

Junxu Li and Sabre Kais, "Entanglement Classifier in Chemical Reactions," Science Advances 5: 5283 (2019)