Description : This course provides an introduction and a practical experience with the emerging field of quantum computing. We focus on the fundamental physical principles, the necessary mathematical background, and provide detailed discussions on the most important quantum algorithms. Some recent applications in Machine learning and Network analysis will be also presented.
In addition, students will learn to use specific software packages and tools that will allow them to implement quantum algorithms on actual cloud-based physical quantum computers .

Skills :
The course aims at providing students with a suitable background to understand the new quantum computing reasoning, and design/analyze quantum algorithms for various application fields. The algorithms will be run on both simulators and real prototypes of quantum machines.

Prerequisites:
Linear algebra, basic concepts of Numerical Analysis, Theory of Algorithms

Syllabus:
● Fundamental Concepts
- Basic mathematical tools (Complex numbers, Hilbert spaces, tensor products properties, unitary matrices, Dirac’s bracket notation)
- Qubits, quantum gates, and circuits
- Superposition and Entanglement
● Fundamental Algorithms
- Teleportation
- Grover’s quantum search algorithm
- Quantum Fourier Transform
- Shor’s integer factorization algorithm
● Recent application
- Quantum Data Preparation and QRAM
- Examples of Quantum Machine Learning algorithms
- Quantum Random Walk
- Quantum Page Rank