17-620: Quantum Machine Learning
Mondays and Wednesdays (5:00pm to 6:20pm @ 3SC 265)
Instructors:
Important Links
Canvas: TBD
Piazza: TBD
Location
Class room is at 3SC 265. The class is in person. I encourage you to come to class. Remote access details, if needed, will be posted later.
Course Goals
Students having already gain familiarity with current universal gate quantum computing tools and technology will expand their knowledge to using Quantum Computing for Machine Learning algorithms.
Materials
Course materials, assignments, and announcements will be posted here and/or on Canvas.
Main Book:
- Practical Quantum Machine Learning and Quantum Optimization
We will be working mostly out of this book.
Extra Reading:
- Machine Learning with Quantum Computers
- Gentle Book
A very computer science take on quantum computers. A good read if you are hoping to do research.
Grading
90% Homeworks/Quizzes, 10% Participation.
Quizzes
Each week a quiz will be given. The worst one will not be counted.
Prerequisites
Python, Jupyter Notebooks, Linear Algebra
Students will not need an understanding of quantum mechanics.
Note: The syllabus is subject to occasional change. This is especially the case in the latter half of the semester once your professors have become comfortable with the group’s overall skill level. Adequate notice will be given.
Basic course structure
This course is a Mini-Course (Half Semester) taking place in the second half of the Fall semester. Due to this being a mini, we will do our best to skirt into a new topic each and every day.
Schedule
Day 1: Introduction
Day 2: Introduction to Pennylane
Day 3: Foundational Concepts of ML & QC
Day 4: QC Algos Relevant for ML - Basics
Day 5: Data Encoding
Day 6: SVMs and NNs - Classical Focus
Day 7: Hybrid Quantum-Classical Algos for ML
Day 8: QML for Optimization and Sampling Problems
Day 9: QSVMS Practical
Day 10: QNNs Practical
Day 11: Guest Lecture
Day 12: Thanksgiving
Day 13: Q-Means
Day 14: Quantum Decoders