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AI+ Quantum™

AT-410

Harness Quantum Power with AI

This comprehensive course provides a deep dive into the intersection of Artificial Intelligence (AI) and Quantum Computing, exploring fundamental concepts, advanced techniques, and ethical considerations. Participants will gain insights into Quantum Computing Gates, Circuits, and Algorithms, with a particular focus on their application in AI domains. Through discussions on Quantum Machine Learning and Quantum Deep Learning, attendees will discover how these technologies are reshaping traditional AI methodologies. Ethical implications are carefully examined throughout, alongside an exploration of current trends and future outlooks. Real-world case studies offer practical insights, while a hands-on workshop solidifies understanding, making this course essential for professionals and enthusiasts alike seeking to navigate and contribute to the transformative landscape of AI and Quantum Computing.

Certification Duration: 40 hours (5 Days)

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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.

Modules

9

Examination

1

50 MCQs

90 Minutes

Passing Score

70%

Certification Modules

  1. Course IntroductionPreview
  1. 1.1 Artificial Intelligence Refresher
  2. 1.2 Quantum Computing Refresher
  1. 2.1 Quantum Gates and their Representation
  2. 2.2 Multi Qubit Systems and Multi Qubit Gates
  1. 3.1 Core Quantum Algorithms
  2. 3.2 QFT and Variational Quantum Algorithms
  1. 4.1 Algorithms for Regression and Classification
  2. 4.2 Algorithms for Dimensionality and Clustering
  1. 5.1 Algorithms for Neural Networks – Part I
  2. 5.2 Algorithms for Neural Networks – Part II
  1. 6.1 Ethics for Artificial Intelligence
  2. 6.2 Ethics for Quantum Computing
  1. 7.1 Current Trends and Tools
  2. 7.2 Future Outlook and Investment
  1. 8.1 Quantum Use Cases
  2. 8.2 QML Case Studies
  1. 9.1 Project – I: QSVM for Iris Dataset
  2. 9.2 Project – II: VQC/QNN on Iris Dataset
  3. 9.3 Bonus: IBM Quantum Computers

Certification Modules

  1. Course IntroductionPreview
  1. 1.1 Artificial Intelligence Refresher
  2. 1.2 Quantum Computing Refresher
  1. 2.1 Quantum Gates and their Representation
  2. 2.2 Multi Qubit Systems and Multi Qubit Gates
  1. 3.1 Core Quantum Algorithms
  2. 3.2 QFT and Variational Quantum Algorithms
  1. 4.1 Algorithms for Regression and Classification
  2. 4.2 Algorithms for Dimensionality and Clustering
  1. 5.1 Algorithms for Neural Networks – Part I
  2. 5.2 Algorithms for Neural Networks – Part II
  1. 6.1 Ethics for Artificial Intelligence
  2. 6.2 Ethics for Quantum Computing
  1. 7.1 Current Trends and Tools
  2. 7.2 Future Outlook and Investment
  1. 8.1 Quantum Use Cases
  2. 8.2 QML Case Studies
  1. 9.1 Project – I: QSVM for Iris Dataset
  2. 9.2 Project – II: VQC/QNN on Iris Dataset
  3. 9.3 Bonus: IBM Quantum Computers

Tools

IBM Qiskit

D-Wave Leap

Google TensorFlow Quantum (TFQ)

Amazon Braket

Exam Objectives

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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.

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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.

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Designing Quantum Circuits

Learners will gain practical skills in designing and constructing quantum circuits, essential for implementing quantum algorithms and solving complex computational problems.

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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.

Career Opportunities Post-Certification

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Median Salaries

$80000
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With AI Skills

$107500
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% Difference

34.38
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Quantum Computing AI Expert

Expert in integrating quantum computing with AI, driving innovations and solutions that leverage quantum mechanics for AI advancements.

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AI Quantum Technology Innovator

Innovates at the forefront of AI and quantum technology, developing groundbreaking solutions that harness the power of both domains.

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Quantum-AI Integration Specialist

Specializes in integrating AI and quantum computing, ensuring seamless interoperability and maximizing the potential of combined technologies.

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AI Quantum Systems Analyst

Analyzes and optimizes quantum-AI systems, ensuring efficient performance and identifying areas for improvement in quantum-enhanced AI applications.

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Frequently Asked Questions

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.

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.

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.

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.

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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