AI+ Quantum™

AI + Quantum Integration: Explore Quantum Gates, Circuits, and AI applications

Advanced Learnings: Includes Quantum Deep Learning and transformative AI methodologies

Industry-Oriented: Real-world case studies and trend analysis

Ethical Focus: Learn implications of quantum AI responsibly and efficiently

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Includes:

Ideal candidates for this course:

Quantum Computing AI Expert. Develop groundbreaking solutions at the intersection of AI and quantum computing.
Quantum-AI Integration Specialist. Specialize in merging AI and quantum computing technologies to maximize their combined potential.
AI Quantum Systems Analyst. Analyze and optimize systems that integrate AI and quantum computing for enhanced performance.
AI Quantum Technology Innovator. Lead innovations by applying quantum mechanics principles to advance AI applications.

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.

Content

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

After completing this course you will be able to:

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