Course Description

This course, being the third installment in the Quantum Computing series, advances into the fascinating domain of quantum algorithms and software. It is designed to expose students to key quantum algorithms developed from fundamental quantum phenomena such as entanglement, and cover varied applications including optimization, quantum chemistry, and machine learning. Significantly, the course provides practical experience with running quantum algorithms on modern quantum hardware via cloud access.

What Students Will Learn

• Understanding and application of the Quantum Fourier Transform and search algorithms.
• Insights into hybrid quantum-classical algorithms and quantum annealing.
• Techniques in quantum simulation and optimization.
• Functional knowledge of quantum machine learning algorithms.
• Experience with cloud-based platforms for quantum programming.

Pre-requisites or Skills Necessary

Completion of Quantum Computing I: Fundamentals is mandatory. Alternatively, substantial experience or knowledge in various aspects of quantum computing such as quantum mechanics, quantum errors, and correction, and NISQ-era technologies is expected.

Additionally, a solid background in undergraduate linear algebra, differential equations, physics, and chemistry is required.

Course Coverage

• Introduction to domain-specific quantum algorithms.
• Exploration of applications in optimization and quantum simulation.
• Detailed study of quantum chemistry and its algorithmic applications.
• Application of machine learning principles in quantum computing.
• Hands-on lab sessions using cloud-based quantum computers.

Who This Course Is For

This course is designed for engineering and natural sciences students, as well as professionals keen on developing and using quantum technologies.

Real-World Applications of Course Skills

The skills taught in this course are highly pertinent in various advanced scientific and industrial fields. Graduates can apply these skills in emerging quantum industries, academia focusing on quantum research, and organizations involved in advanced computing research and implementation.

Syllabus

• Week 1: Introduction to Quantum Algorithms
• Week 2: Quantum Fourier Transform and its Applications
• Week 3: Advanced Quantum Algorithms for Optimization
• Week 4: Quantum Simulation Methods
• Week 5: Quantum Chemistry Algorithms
• Week 6: Quantum Machine Learning Algorithms
• Week 7: Cloud-based Quantum Computing
• Week 8: Capstone Project in Quantum Algorithm Application