11-860: Quantum Computing, Cryptography, & Machine Learning Lab

Course Overview

This page will be updated with the Spring 2026 schedule, logistics, and materials.

Logistics

Monday and Wednesday (5:00pm to 6:20pm EST @ GHC 4215)

Instructors: Daniel Justice, Bhiksha Raj, Rita Singh

TA: TBD

Location

GHC 4215

Important Links

Piazza: TBD (announcements and communication).
Canvas: TBD (assignments and course materials).

Course Goals

Students will gain familiarity with current universal gate quantum computing tools and technology. Students will also become comfortable with several QC algorithms and their implementation on state of the art quantum computer simulators and hardware.

Grading

30% Homeworks, 30% Group Project, 10% Participation, 30% weekly quizzes.

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

Most weeks are designed to be 1 day of lecture and 1 day of lab.

Schedule

The first class is January 12. Dates follow the CMU 2025-26 academic calendar.

01/12: Transition from Classical to Quantum Computing - Lecture
01/14: Measurement, No Cloning and BB84 - Lecture
01/19: Martin Luther King Jr. Day - No Lecture
01/21: Lab
01/26: 1-Qubit Gates - Lecture
01/28: Lab
02/02: CHSH and No Communication - Lecture
02/04: Lab
02/09: Multi-Qubit Gates and Quantum Circuits - Lecture
02/11: Lab
02/16: Entanglement and Quantum Teleportation - Lecture
02/18: Lab
02/23: Crypt/NVIDIA/Classiq/QuEra - Lecture
02/25: Deutsche’s Algorithm - Lecture
03/02: Spring Break
03/04: Spring Break
03/09: Lab
03/11: Simon’s Algorithm - Lecture
03/16: Lab
03/18: Grover’s Algorithm - Lecture
03/23: Lab
03/25: Shor’s Algorithm - Lecture
03/30: Lab
04/01: Data Encoding - Lecture
04/06: Lab
04/08: Inner Products and Linear Classifiers
04/13: Lab
04/15: Quantum Kernels and Neural Networks - Lecture
04/20: Lab
04/22: Losses and Training