AI+ Quantum Practitioner™
- From 505 EUR + VAT
- Self-paced online Start anytime
- Duration 40 hours of content, self-paced
- Language English
- Topic AI Data & Robotics
Overview
What the course covers
The course, the materials and the exam are in English. The description below is the original AI CERTs® text.
- 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
Basic understanding of AI concepts, Problem-solving mindset in AI and Quantum, Openness to ethical considerations in AI and quantum practices.
Audience
Who is it for
Quantum Computing Engineers: Enhance quantum system design and performance using AI for optimization and control.
Physics Engineers: Apply AI techniques to improve quantum simulations and computational models.
AI Specialists: Leverage AI and quantum algorithms to create intelligent solutions for complex problems.
IT Specialists & System Integrators: Integrate AI-driven quantum computing systems to optimize infrastructure and solve large-scale challenges.
Students & New Graduates: Gain foundational skills in AI and quantum computing to excel in the rapidly advancing quantum technology field.
In the price
What the price includes
- Self-paced online course with the full learning material
- The official AI CERTs® exam
- Digital badge after passing the exam
Course content
Syllabus
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
Exam
About the exam
- 50 Questions
- 90 min Time limit
- 70% Pass mark
- Online proctored exam
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