17-628 — Applied Quantum Computing — Spring Semester 2024

Mondays and Wednesdays (TBD @ 3SC 265)

Derek Wang

Daniel Justice

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.

Quantum computers can solve specific problems more efficiently than their classical counterparts in theory. But in practice, today’s quantum devices are too small and noisy to run many flagship algorithms. However, with carefully crafted hardware-software-algorithm-application stacks, quantum computers can already be used as interesting scientific tools for applications including fundamental physics, chemistry, and machine learning. In this course, we will learn about quantum applications in practice by understanding each component of a full-stack quantum computer. First, we will survey potential applications for quantum computers and identify which ones are both feasible and useful in the near-term. We will focus on applications involving simulation and machine learning, and then dive deep into relevant hybrid quantum-classical and all-quantum algorithms. Then, to achieve a deep understanding of the need for hardware-efficient algorithms, we will survey various physical platforms for quantum computers coupled with strategies for mitigating quantum errors based on fundamental concepts in quantum information theory. Throughout the course, concepts will be reinforced through practical coding exercises using modern software tools that will culminate in a final project to implement a quantum application at the cutting-edge of the field.

80% Homeworks/Quizzes, 10% Participation by instructors, 10% Participation by classmates

Each week a quiz will be given. The worst one will not be counted.

Basic Quantum Computing Experience

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.

Due to this being a mini, we will do our best to skirt into a new topic each and every day.

**Day 1** (03/11): Introduction slides

**Day 2** (03/13): Foundations slides

**Day 3** (03/18): Algorithms: Time Evolution slides

**Day 4** (03/20): Algorithms: Time Evolution 2 slides

**Day 5** (03/25): Adiabatic Quantum Computation slides

**Day 6** (03/27): Quantum Approximation Optimization Algorithm slides

**Day 7** (04/01): Variational Quantum Machine Learning slides

**Day 8** (04/03): Noise slides

**Day 9** (04/08): NO CLASS! HAPPY ECLIPSE DAY!

**Day 10** (04/10): Kernel-based Quantum Machine Learning slides

**Day 11** (04/15): Error Mitigation slides

**Day 12** (04/17): Towards Fault Tolerance slides

**Day 13** (04/22): Guest Lecture - Jim Garrison

**Day 14** (04/24): Dealers Choice

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