Quantum data and machine learning for quantum chemistry

In this project, the university and industry researchers will work together to examine ways in which two new, powerful computing technologies, machine learning, and quantum computing, can be combined in order to improve our ability to understand and simulate molecules, chemistry and materials. One of the key challenges that limits improvements to today’s chemical simulation software is the requirement for high quality experimental data for systems of interest. Machine learning has the potential to take limited data sets and learn from them, extending reach to new systems. Quantum computing has the potential to more accurately and efficiently simulate the quantum mechanics of molecules than traditional computers. This project will explore new ways to combine these approaches to advance the field of chemical simulations.

Faculty Supervisor:

Nathan Wiebe

Student:

Partner:

Xanadu

Discipline:

Computer science

Sector:

Information and cultural industries; Manufacturing; Professional, scientific and technical services

University:

University of Toronto

Program: