ECE 59500 - Applied Quantum Computing III-Algorthm and SoftwareLecture Hours: 3 Credits: 1
Areas of Specialization(s):Fields and Optics
Microelectronics and Nanotechnology
CMPE Special Content Elective
Experimental Course Offered: Spring 2021
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
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.
This is a 1-credit, 5-week course that will run weeks 11-15 of the semester.
Required Text(s): None.
Recommended Text(s): None.
|1||Quantum Fourier transform and search algorithms|
|2||Hybrid quantum-classical algorithms|
|3||Quantum annealing and optimization|
|5||Quantum machine learning|