Our training approach is human‑centred and outcomes‑driven. We focus on what learners can apply confidently.
Learners will acquire skills in developing quantum algorithms specifically designed for AI applications. This involves creating and implementing quantum circuits and understanding how quantum gates operate within these algorithms.
Learners will learn how to apply quantum computing principles to machine learning and deep learning models. This includes the development and optimization of quantum-enhanced models that leverage the unique advantages of quantum computing.
Learners will gain practical skills in designing and constructing quantum circuits, essential for implementing quantum algorithms and solving complex computational problems.
Learners will learn techniques to optimize quantum-AI models for better performance, including fine-tuning parameters and reducing computational complexity.
Develop groundbreaking solutions at the intersection of AI and quantum computing.
Specialize in merging AI and quantum computing technologies to maximize their combined potential.
Analyze and optimize systems that integrate AI and quantum computing for enhanced performance.
Lead innovations by applying quantum mechanics principles to advance AI applications.
70%
50 multiple-choice/multiple-response questions
| Overview of Artificial Intelligence (AI) and Quantum Computing | 5% |
| Quantum Computing Gates, Circuits, and Algorithms | 11% |
| Quantum Algorithms for AI | 12% |
| Quantum Machine Learning | 12% |
| Quantum Deep Learning | 12% |
| Ethical Considerations | 12% |
| Trends and Outlook | 12% |
| Use Cases & Case Studies | 12% |
| Workshop | 12% |
IBM Qiskit
D-Wave Leap
Google TensorFlow Quantum (TFQ)
Amazon Braket