AI+ Quantum™
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
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.
- 1.1 Artificial Intelligence Refresher
- 1.2 Quantum Computing Refresher
- 2.1 Quantum Gates and their Representation
- 2.2 Multi Qubit Systems and Multi Qubit Gates
- 3.1 Core Quantum Algorithms
- 3.2 QFT and Variational Quantum Algorithms
- 4.1 Algorithms for Regression and Classification
- 4.2 Algorithms for Dimensionality and Clustering
- 5.1 Algorithms for Neural Networks – Part I
- 5.2 Algorithms for Neural Networks – Part II
- 6.1 Ethics for Artificial Intelligence
- 6.2 Ethics for Quantum Computing
- 7.1 Current Trends and Tools
- 7.2 Future Outlook and Investment
- 8.1 Quantum Use Cases
- 8.2 QML Case Studies
- 9.1 Project – I: QSVM for Iris Dataset
- 9.2 Project – II: VQC/QNN on Iris Dataset
- 9.3 Bonus: IBM Quantum Computers
- 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
IBM Qiskit
D-Wave Leap
Google TensorFlow Quantum (TFQ)
Amazon Braket
Passing Score : 70% (35/50)
Number of Multiple Choice Question : 50 MCQs, 90 Minutes
Number of exams required for certification : 1
Exam Blueprint
- Overview of Artificial Intelligence (AI) and Quantum Computing – 8%
- Quantum Computing Gates, Circuits, and Algorithms – 10%
- Quantum Algorithms for AI – 10%
- Quantum Machine Learning – 11%
- Quantum Deep Learning – 11%
- Ethical Considerations – 11%
- Trends and Outlook – 11%
- Use Cases & Case Studies – 11%
- Workshop – 11%
Quantum Algorithm Development
Learners will acquire skills in developing quantum algorithms specifically designed for AI applications. This involves creating and implementing quantum circuits and understanding how quantum gates operate within these algorithms.
Quantum Machine Learning and Deep Learning
Learners will learn how to apply quantum computing principles to machine learning and deep learning models. This includes the development and optimization of quantum-enhanced models that leverage the unique advantages of quantum computing.
Designing Quantum Circuits
Learners will gain practical skills in designing and constructing quantum circuits, essential for implementing quantum algorithms and solving complex computational problems.
Optimization of Quantum-AI Models
Learners will learn techniques to optimize quantum-AI models for better performance, including fine-tuning parameters and reducing computational complexity.
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.
FAQ
Are there hands-on components in the course?
Yes, the course includes a hands-on workshop to reinforce theoretical concepts. Participants will engage in practical exercises to apply Quantum Computing principles to AI scenarios, enhancing their understanding through real-world applications.
Who should enroll in this AI+ Quantum™ course?
This course is for professionals and enthusiasts with a basic understanding of AI, eager to explore AI and Quantum Computing technologies for innovative problem-solving.
What career opportunities does this course open up?
Graduates of this course are equipped to contribute to industries undergoing rapid transformation, including healthcare, finance, cybersecurity, and logistics, where AI and Quantum Computing are driving innovative solutions and advancements.
What are the benefits of learning about AI and Quantum Computing together?
By understanding both AI and Quantum Computing, participants gain insights into cutting-edge technologies that complement each other. This interdisciplinary knowledge equips them to innovate and solve complex problems more effectively across various industries.
How practical are the skills learned in this course for real-world applications?
The course emphasizes practical applications through hands-on workshops and real-world case studies. Participants gain experience in implementing Quantum Computing algorithms and techniques, enhancing their readiness to tackle industry challenges.
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