Course Prerequisites:
A foundational knowledge of AI concepts, no technical skills are required.
Willingness to exploring unconventional approaches to problem-solving within the context of AI and Quantum.
Openness to engage critically with ethical dilemmas and considerations related to AI technology in quantum practices.
Modules:
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