Recommended Prerequisites:
- Foundational Knowledge of AI: A basic understanding of AI concepts is helpful, but no technical skills are required.
- Willingness to Explore Quantum Approaches: An open mindset to tackle unconventional problem-solving in AI and Quantum Computing.
- Engagement with Ethical Considerations: A readiness to critically analyze ethical dilemmas in the application of AI in quantum systems.
Course Outline:
Lesson 1: Overview of Artificial Intelligence (AI) and Quantum Computing
- 1.1 Artificial Intelligence Refresher
- 1.2 Quantum Computing Refresher
Lesson 2: Quantum Computing Gates, Circuits, and Algorithms
- 2.1 Quantum Gates and their Representation
- 2.2 Multi Qubit Systems and Multi Qubit Gates
Lesson 3: Quantum Algorithms for AI
- 3.1 Core Quantum Algorithms
- 3.2 QFT and Variational Quantum Algorithms
Lesson 4: Quantum Machine Learning
- 4.1 Algorithms for Regression and Classification
- 4.2 Algorithms for Dimensionality and Clustering
Lesson 5: Quantum Deep Learning
- 5.1 Algorithms for Neural Networks – Part I
- 5.2 Algorithms for Neural Networks – Part II
Lesson 6: Ethical Considerations
- 6.1 Ethics for Artificial Intelligence
- 6.2 Ethics for Quantum Computing
Lesson 7: Trends and Outlook
- 7.1 Current Trends and Tools
- 7.2 Future Outlook and Investment
Lesson 8: Use Cases & Case Studies
- 8.1 Quantum Use Cases
- 8.2 QML Case Studies
Lesson 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 Lesson: AI Agents for Quantum
- What Are AI Agents
- Key Capabilities of AI Agents in Quantum Computing
- Applications and Trends for AI Agents in Quantum Computing
- How Does an AI Agent Work
- Core Characteristics of AI Agents
- Types of AI Agents
