17-620 — Quantum Machine Learning — Fall Semester 2023

Quantum Machine Learning

Mondays and Wednesdays (5:00pm to 6:20pm @ 3SC 265)

Instructors:

Daniel Justice & Rita Singh

Location

Class room is at 3SC 265. The class is in person but will also be streamed via Zoom. I encourage you to come to class and use Zoom as a fallback option.

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.

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 (10/23): Introduction
Day 2 (10/25): The Landscape of ML & SVM
Day 3 (10/30): Building a Neural Network
Day 4 (11/1): Binary and Multiclass Classification
Day 5 (11/6): Intro to Pennylane
Day 6 (11/8): QSVM
Day 7 (11/13): QNN
Day 8 (11/15): Guest Lecture
Day 9 (11/20): GANs
Day 10 (11/22): Thanksgiving
Day 11 (11/27): Q-GANs
Day 12 (11/29): K-Means
Day 13 (12/4): Q-Means
Day 14 (12/6): Quantum Decoders

Schedule with assignments, readings, etc. can be found here