AI Data & Robotics

AI+ Quantum

This course covers AI and Quantum Computing fundamentals, Quantum Machine Learning, ethics, and real-world applications. It includes case studies and a hands-on workshop for practical learning.

Duration

5 Day (40 hours)

Targeted Audience

Quantum AI Integration Specialist

Quantum System Analyst

Quantum Computing AI Expert

AI Quantum Technology Innovator

Prerequisites for Success

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 | 9

Examination | 1

Passing Score | 70%

COURSE AGENDA

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 Artificial Intelligence (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