ECE 59500 - Applied Quantum Computing III-Algorthm and Software


This is a 1-credit, 5-week course that will run weeks 11-15 of the semester.

Course Details

Lecture Hours: 3 Credits: 1

Areas of Specialization:

  • Fields and Optics
  • Microelectronics and Nanotechnology

Counts as:

  • EE Elective
  • CMPE Selective - Special Content

Normally Offered:

Each Spring


On-campus and online


Applied Quantum Computing I: Fundamentals, ECE 20875, PHYS 17200, MA 26500 and MA 26600 (or MA 26200)

Requisites by Topic:

Fundamentals of Applied Quantum Computing, python, basic mechanics, linear algebra, differential equations

Catalog Description:

This course is part III of the series of Quantum computing courses, which covers aspects from fundamentals to present-day hardware platforms to quantum software and programming. The goal of part III is to discuss some of the key domain-specific algorithms that are developed by exploiting the fundamental quantum phenomena (e.g. entanglement)and computing models discussed in part I. We will begin by discussing classic examples of quantum Fourier transform and search algorithms, along with its application for factorization (the famous Shor???s algorithm). Next, we will focus on the more recently developed algorithms focusing on applications to optimization, quantum simulation, quantum chemistry, machine learning, and data science. A particularly exciting recent development has been the emergence of near-intermediate scale quantum (NISQ) computers. We will also discuss how these machines are driving new algorithmic development. A key aspect of the course is to provide hands-on training for running (few qubit instances of) the quantum algorithms on present-day quantum hardware. For this purpose, we will take advantage of the availability of cloud-based access to quantum computers and quantum software. The material will appeal to engineering students, natural sciences students, and professionals whose interests are in using as well as developing quantum technologies.

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Learning Outcomes

A student who successfully fulfills the course requirements will have demonstrated an ability to:

  • apply principles of quantum mechanics to design quantum algorithms for computing applications

Lecture Outline:

Week Topic
1 Quantum Fourier transform and search algorithms
2 Hybrid quantum-classical algorithms
3 Quantum annealing and optimization
4 Quantum chemistry
5 Quantum machine learning

Assessment Method:

Quizzes, exam