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Dai, Jun

University of British Columbia

This thesis investigates applications of classical machine learning to quantum problems and the possibilities of combining machine learning and quantum computing to improve algorithms for solving quantum problems. In quantum physics and quantum chemistry, the high dimensionality of quantum problems poses a significant challenge. Due to the increased complexity of such problems, traditional algorithms may not solve them effectively. However, new insights and better computational methods have become possible with the development of machine learning methods. This thesis aims to develop new methods based on classical and quantum machine learning methods applied to quantum problems. The first part of the thesis shows how Bayesian machine learning can be applied to quantum research when the number of calculations is limited. To be more specific, I construct accurate global potential energy surfaces for polyatomic systems by using a small number of energy points and demonstrate methods to improve the accuracy of quantum dynamics approximations with few exact results. The second part of the thesis looks into combining machine learning and quantum computing to improve machine learning algorithms. I demonstrate the first practical application of quantum regression models and use the resulting models to produce accurate global potential energy surfaces for polyatomic molecules. Furthermore, I illustrate the effect of qubit entanglement for the resulting models. In addition, I propose a quantum-enhanced feature mapping algorithm that is proven to have a quantum advantage for specific classically unsolvable classification problems and is more computationally efficient than previous methods. Finally, I highlight the potential for combining machine learning and quantum computing to improve algorithms for solving quantum problems.

Text

2023-06-21

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/85128

Chemistry

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/