The AI+ Quantum™ certification bridges the gap between Artificial Intelligence and Quantum Computing, offering an in-depth exploration of cutting-edge technologies. The course delves into Quantum Computing Gates, Circuits, and Algorithms, with a focus on real-world AI applications. Participants will explore advanced topics like Quantum Machine Learning, Quantum Deep Learning, and their transformative impact on traditional AI methodologies. This program critically examines ethical implications, explores industry trends, and provides hands-on workshops to solidify knowledge. Real-world case studies offer practical insights, making this certification essential for professionals and enthusiasts aiming to lead in the evolving field of AI and Quantum Computing.
Quantum Algorithm Development: Learn to design and implement quantum algorithms optimized for AI applications, including how quantum gates operate within these frameworks.
Designing Quantum Circuits: Gain practical skills in constructing and optimizing quantum circuits to solve complex computational challenges.
Quantum Machine Learning and Deep Learning: Discover how quantum principles can enhance machine learning and deep learning models for superior performance.
Optimisation of Quantum-AI Models: Master techniques to fine-tune quantum-AI systems, improving efficiency and reducing computational complexity.
AI and Quantum Computing Professionals: Individuals looking to deepen their expertise at the intersection of AI and Quantum Computing.
Tech Innovators and Researchers: Those eager to explore cutting-edge technologies and contribute to the advancement of AI and Quantumapplications.
Data Scientists and Engineers: Experts aiming to expand their knowledge in QML and QDL techniques
Module 1: Overview of Artificial Intelligence (AI) and Quantum Computing
1.1 Artificial Intelligence Refresher
1.2 Quantum Computing Refresher
Module 2: Quantum Computing Gates, Circuits, and Algorithms
2.1 Quantum Gates and their Representation
2.2 Multi Qubit Systems and Multi Qubit Gates
Module 3: Quantum Algorithms for AI
3.1 Core Quantum Algorithms
3.2 QFT and Variational Quantum Algorithms
Module 4: Quantum Machine Learning
4.1 Algorithms for Regression and Classification
4.2 Algorithms for Dimensionality and Clustering
Module 5: Quantum Deep Learning
5.1 Algorithms for Neural Networks – Part I
5.2 Algorithms for Neural Networks – Part II
Module 6: Ethical Considerations
6.1 Ethics for Artificial Intelligence
6.2 Ethics for Quantum Computing
Module 7: Trends and Outlook
7.1 Current Trends and Tools
7.2 Future Outlook and Investment
Module 8: Use Cases & Case Studies
8.1 Quantum Use Cases
8.2 QML Case Studies
Module 9: Workshop
9.1 Project – I: QSVM for Iris Dataset
9.2 Project – II: VQC/QNN on Iris Dataset
9.3 Bonus: IBM Quantum Computers
Optional Module: AI Agents for Quantum
1. What Are AI Agents
2. Key Capabilities of AI Agents in Quantum Computing
3. Applications and Trends for AI Agents in Quantum Computing
4. How Does an AI Agent Work
5. Core Characteristics of AI Agents
6. Types of AI Agents