Applied Quantum Computing III: Algorithm and Software (edX)

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 course is part of the Quantum Technology: Computing MicroMasters and Quantum Technology: Detectors and Networking MicroMasters.

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.

Prerequisites:
Quantum Computing I: Fundamentals (edX course) ; Undergraduate linear algebra, Python, physics, and chemistry

What you'll learn

- Quantum Fourier transform and search algorithms

- Hybrid quantum-classical algorithms

- Quantum annealing, simulation, and optimization algorithms

- Quantum machine-learning algorithms

- Cloud-based quantum programming