course | Quantum Machine Learning
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IT-1709 | Quantum Machine Learning
Course Sector : Information Technology
| Duration | Date from | Date to | Course Venue | Course fees | Book a course |
|---|---|---|---|---|---|
| 5 Days | 2025-04-14 | 2025-04-18 | Dubai | $4,250 | Book now |
| 5 Days | 2025-08-18 | 2025-08-22 | Geneva | $5,950 | Book now |
| 5 Days | 2025-10-20 | 2025-10-24 | Dubai | $4,250 | Book now |
Course Introduction
Quantum Machine Learning (QML) merges quantum computing with machine learning to address complex problems and enhance computational capabilities. This course will explore how quantum computing can revolutionize machine learning algorithms and models, providing practical insights and hands-on experience in this cutting-edge field.
Course objective
- Understand the principles of quantum computing and how they apply to machine learning.
- Explore quantum machine learning algorithms and their advantages.
- Gain hands-on experience with quantum machine learning tools and frameworks.
- Apply quantum algorithms to solve real-world machine learning problems.
- Analyze the potential and challenges of quantum machine learning in various domains.
Course Outline | Day 01
Module 1: Introduction to Quantum Computing and Machine Learning
- Basics of Quantum Computing and Quantum Bits (Qubits)
- Overview of Machine Learning Concepts and Algorithms
- How Quantum Computing Enhances Machine Learning
- Hands-On Exercise: Visualizing Quantum Concepts and Machine Learning Models
- Case Study: Applications of Quantum Computing in Machine Learning
Course Outline | Day 02
Module 2: Quantum Algorithms for Machine Learning
- Quantum Algorithms Overview (e.g., Quantum Support Vector Machines, Quantum Neural Networks)
- Quantum Data Encoding and Feature Mapping
- Quantum Variational Algorithms (e.g., Variational Quantum Eigensolver)
- Hands-On Exercise: Implementing Basic Quantum Machine Learning Algorithms
Case Study: Real-World Applications and Benefits of Quantum Algorithms
Course Outline | Day 03
Module 3: Quantum Machine Learning Frameworks and Tools
- Introduction to Quantum Machine Learning Frameworks (e.g., TensorFlow Quantum, PennyLane)
- Quantum Computing Platforms and Tools (e.g., IBM Qiskit, Google Cirq)
- Developing and Testing Quantum Machine Learning Models
- Hands-On Exercise: Using Quantum Frameworks to Build and Train Models
- Case Study: Tools and Frameworks in Quantum Machine Learning Projects
Course Outline | Day 04
Module 4: Advanced Quantum Machine Learning Techniques
- Quantum Data Analysis and Feature Extraction
- Quantum-enhanced Reinforcement Learning
- Hybrid Quantum-Classical Machine Learning Models
- Hands-On Exercise: Developing Advanced Quantum Machine Learning Models
- Case Study: Innovations and Advanced Techniques in Quantum Machine Learning
Course Outline | Day 05
Module 5: Future Trends and Challenges in Quantum Machine Learning
- Emerging Trends and Research Directions in Quantum Machine Learning
- Challenges and Limitations of Quantum Machine Learning
- Potential Applications and Industry Impact
- Hands-On Exercise: Exploring Future Scenarios and Innovations
- Case Study: Cutting-Edge Projects and the Future of Quantum Machine Learning
BOOST’s Professional Attendance Certificate “BPAC”
BPAC is always given to the delegates after completing the training course,and depends on their attendance of the program at a rate of no less than 80%,besides their active participation and engagement during the program sessions.
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