Quantum Machine Learning: Harnessing Quantum Computing for AI
Quantum Computing, Machine Learning, Quantum Algorithms
Skills you will gain:
It is an 8-week intensive course designed for advanced undergraduates, graduate students, and professionals interested in the intersection of quantum computing and AI. Through comprehensive tutorials, hands-on workshops, and live demonstrations, participants will learn to simulate quantum circuits, develop quantum algorithms, and apply these in AI tasks. The course offers a unique opportunity to explore this cutting-edge technology, preparing participants to contribute to the future of quantum machine learning.
Aim: The course aims to provide participants with a comprehensive understanding of quantum computing fundamentals and their applications in machine learning. This course strives to bridge the gap between quantum technologies and artificial intelligence, empowering participants to develop and implement quantum machine learning algorithms that can outperform classical solutions in speed and efficiency.
Program Objectives:
- Understand Quantum Computing and AI: Build a solid foundation in the principles of quantum mechanics and its applications to solve complex computational problems.
- Develop Quantum Algorithms: Learn and develop quantum machine learning algorithms, such as quantum neural networks and quantum support vector machines.
- Practical Simulation Experience: Gain hands-on experience with quantum simulation software to design and test quantum circuits.
- Project Implementation: Plan and execute a quantum machine learning project from inception to testing and optimization on quantum computing platforms.
- Industry Readiness: Prepare for emerging roles in the quantum technology sector by understanding current technologies, potential applications, and industry needs.
What you will learn?
Module 1: Introduction to Quantum Computing and Machine Learning
Section 1: Foundations of Quantum Computing
- Principles of Quantum Mechanics
- Quantum Bits and Quantum Gates
- Quantum Circuits
Section 2: Basics of Machine Learning
- Overview of Machine Learning
- Algorithms and Models in Classical Machine Learning
- Data Handling and Preprocessing
Section 3: Integration of Quantum Computing with Machine Learning
- Potential of Quantum Computing in AI
- Case Studies: Quantum Speedups in Algorithmic Tasks
Module 2: Quantum Algorithms for Machine Learning
Section 1: Quantum Algorithms Basics
- Grover’s Algorithm
- Shor’s Algorithm
- Quantum Fourier Transform
Section 2: Quantum Machine Learning Algorithms
- Quantum Principal Component Analysis
- Quantum Support Vector Machines
- Quantum Neural Networks
Module 3: Implementing Quantum ML Models
Section 1: Quantum Programming Languages
- Qiskit
- Microsoft Q#
- Forest by Rigetti
Section 2: Simulation and Real Quantum Computers
- Using IBM Quantum Experience
- Cloud-based Quantum Computing
Section 3: Project Development
- Project Planning and Design
- Implementation of a Quantum ML Model
- Testing and Optimization on Quantum Devices
Intended For :
This course is suitable for advanced undergraduates and graduate students in Computer Science, IT, and Electronics, as well as professionals in IT services, Consulting, and Analytics services looking to explore new technologies. Basic understanding of quantum computing and machine learning concepts is recommended.
Career Supporting Skills
