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Quantum Machine Learning: Harnessing Quantum Computing for AI
Quantum Computing, Machine Learning, Quantum Algorithms
Early access to the e-LMS platform is included
About This Course
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, 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.
- 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.
Program Structure
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
Who Should Enrol?
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.
Program Outcomes
Upon completion of this course, participants will:
- Gain a robust understanding of Quantum Machine Learning and its applications in AI.
- Develop practical skills in implementing machine learning algorithms on quantum devices.
- Acquire hands-on experience through simulations of quantum circuits and real-world projects.
- Explore the integration of quantum computing with machine learning to solve complex problems.
- Be prepared to contribute to the rapidly growing field of Quantum Machine Learning in academia or industry.
Fee Structure
Discounted: ₹21499 | $291
We accept 20+ global currencies. View list →
What You’ll Gain
- Full access to e-LMS
- Real-world dry lab projects
- One-on-one project guidance
- Publication opportunity
- Self-assessment & final exam
- e-Certificate & e-Marksheet
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