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
Tools you will explore
- IBM Qiskit
- D-Wave Leap
- Google TensorFlow Quantum (TFQ)
- Amazon Braket
