Introduction to Quantum Computing

Catalog description

This course is designed for graduate and advanced undergraduate students. It introduces integer programming and machine learning as non-quantum (classical) solution methods for certain classes of difficult problems, and then discusses concepts that take advantage of near-term quantum and quantum-inspired computing. The course covers recent developments in quantum computing for solution of combinatorial optimization problems using machine learning methods. Quantum computing is discussed within the circuit-based and the annealing-based models of computing currently implemented on various hardware incarnations. Potential use of quantum processors for solution of machine learning algorithms designed for difficult problems that classical computers struggle with is explored. The format of the course is a series of lectures and problem sets that expose students to practical exercises using cloud-based quantum computing resources that include quantum annealing machines and circuit-based quantum processors. Students will gain experience and exposure to these novel computing resources through cloud-based quantum computing platforms at Amazon Web Services. The main deliverable for the course is a final group project report and an oral presentation that offer students an opportunity to thoroughly familiarize themselves with a problem of their choosing and interest. Students will use classical and unconventional (quantum and quantum-inspired) computing tools to address the problem in a specific application area of their choice. Prerequisites: ( 18-202 Min. grade B or 21-240 Min. grade B) and 21-241 Min. grade B and 36-219 Min. grade B