AI+ Quantum™

Harness Quantum Power with AI

• 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

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

Exam: 50 questions, 70% passing, 90 minutes, online proctored examInstructor-led OR Self-paced course + Official exam + Digital badge