Units:

12

Description:

This course is primarily designed for graduate students (and advanced undergraduates) interested in understanding the potential of near-term quantum and quantum-inspired computing for solving certain classes of problems, for example in engineering, science, or finance. The first part of the course discusses integer programming (IP) with non-linear objective functions and machine learning (ML) and the potential of near-term quantum and quantum-inspired computing for solving them.

The second part of the course briefly reviews aspects of classical machine learning that impose tremendous computational demands (of processing of massive data with parallelism, high speeds and low power) on conventional computers. It then examines how emerging quantum computer hardware and quantum-inspired concepts could help solve certain classes of problems in ML that classical computer hardware struggle with.

By the end of the semester, someone enrolled in this course should be able to:

Appreciate the current status of quantum computing and its potential use for integer programming and machine learning
Access and use quantum computing resources (such as D-Wave Quantum Annealers)
Set up a given integer program or a machine learning problem to be solved with quantum computing

Last Modified: 2024-06-28 12:48PM

Current session:

This course is currently being offered.

Semesters offered:

• Fall 2024
• Fall 2023
• Fall 2022
• Fall 2021
• Fall 2015
• Spring 2013