CS 8803 O20: Quantum Hardware

Instructional Team

Buzz
Asif Khan
Creator, Instructor
Colin Parker
Colin Parker
Creator, Instructor

Overview

Quantum computing promises exponential speedups for a class of important problems. Quantum computers with hundreds of qubits have already been demonstrated, and qubit counts are expected to cross into the thousand in the next few years. Quantum Computing is an interdisciplinary field with topics ranging from physical devices (ion traps, superconducting circuits, spins, etc.) to system and architecture issues (memory/microarchitecture/IO) to algorithms and applications.

The goal of this course is to provide CS students with a fundamental background in the hardware aspects of quantum computing and to equip them with the skills needed to work on hardware and software systems that implement and support the next generation of quantum devices. Special focus will be on the physical method of operation of current and proposed quantum devices, explained with the mathematical structure of quantum information, rather than theoretical physics.

This course is not foundational and does not count toward any specializations at present, but it can be counted as a free elective.

Course Goals

By the end of this course, students will:

  1. Become familiar with the dominant qubit technologies and understand their relative advantage and disadvantages in terms of scalability, including the following platforms:
    1. Superconducting circuits (transmons)
    2. Trapped ions
    3. Trapped neutral atoms
    4. Semiconductor quantum dot
  2. Become familiar with how different single- and multi-qubit operations are performed physically, including:
    1. ISWAP gate (superconducting circuit)
    2. Cirac-Zoller gate (ion trap)
    3. Mølmer–Sørensen gate (ion trap)
    4. Geometric phase gate (ion trap)
    5. Rydberg blockade gate (neutral atom)
  3. Understand how to create quantum superposition and entangle qubits at the hardware level for quantum algorithms
  4. Understand the problem of quantum noise, its origin, and schemes to benchmark quantum error
  5. Become familiar with advanced/exploratory qubit concepts, including photonic quantum computing and topological qubits
  6. Understand the critical challenges for scaling up quantum computers from a system-level hardware perspective
  7. Write code using IBM’s qiskit platform using expanded Hilbert spaces to simulate a more complete description of quantum platforms

Sample Syllabus

Summer 2025 syllabus (PDF)

Note: Sample syllabi are provided for informational purposes only. For the most up-to-date information, consult the official course documentation.

Before Taking This Class...

Suggested Background Knowledge

This is a graduate-level course that assumes familiarity with college level calculus, complex numbers, and linear algebra topics including vector spaces, inner product spaces, matrix manipulation, diagonalization, and eigenvalues/vectors. Prior exposure to quantum computing topics, such as quantum algorithms, provides useful background but is not strictly required. No prior knowledge of physics is assumed.

Technical Requirements and Software

Students will need a computer capable of running Python and qiskit.

Academic Integrity

All Georgia Tech students are expected to uphold the Georgia Tech Academic Honor Code. This course may impose additional academic integrity stipulations; consult the official course documentation for more information.