Key Information
About the content
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.
Attention:
Quantum Computing 1: Fundamentals is an essential prerequisite to Quantum Computing 2: Hardware and Quantum Computing 3: Algorithm and Software. Learners should plan to complete Fundamentals (1) before enrolling in the Hardware (2) or the Algorithm and Software (3) courses.
Alternatively, learners can enroll in courses 2 or 3 if they have solid experience with or knowledge of quantum computing fundamentals, including the following: 1) postulates of quantum mechanics; 2) gate-based quantum computing; 3) quantum errors and error correction; 3) adiabatic quantum computing; and 5) quantum applications and NISQ-era.
Prerequisite
Applied Quantum Computing I: Fundamentals
Undergraduate linear algebra, differential equations, physics, and chemistry.
Syllabus
-
Quantum Fourier transform and search algorithms
-
Hybrid quantum-classical algorithms
-
Quantum annealing, simulation, and optimization algorithms
-
Quantum machine-learning algorithms
-
Cloud-based quantum programming
Instructors
Pramey Upadhyaya
Assistant Professor of Electrical and Computer Engineering
Purdue University
Avinash Rustagi
Post-doctoral Research Associate, Department of Electrical and Computer Engineering
Purdue University
Platform
Harvard University, the Massachusetts Institute of Technology, and the University of California, Berkeley, are just some of the schools that you have at your fingertips with EdX. Through massive open online courses (MOOCs) from the world's best universities, you can develop your knowledge in literature, math, history, food and nutrition, and more. These online classes are taught by highly-regarded experts in the field. If you take a class on computer science through Harvard, you may be taught by David J. Malan, a senior lecturer on computer science at Harvard University for the School of Engineering and Applied Sciences. But there's not just one professor - you have access to the entire teaching staff, allowing you to receive feedback on assignments straight from the experts. Pursue a Verified Certificate to document your achievements and use your coursework for job and school applications, promotions, and more. EdX also works with top universities to conduct research, allowing them to learn more about learning. Using their findings, edX is able to provide students with the best and most effective courses, constantly enhancing the student experience.
