AI+ Quantum Practitioner™

Course code: NPAT410

Formerly known as AI+ Quantum™

Harness Quantum Power with AI

  • AI + Quantum Integration: Dive into Quantum Gates, Circuits, and their intersection with AI applications.
  • Advanced Learnings: Encompasses Quantum Deep Learning and cutting-edge AI methodologies driving transformation.
  • Industry-Oriented: Grounded in real-world case studies and current trend analysis across sectors.
  • Ethical Focus: Examine the implications of quantum AI with a responsible and efficiency-driven mindset.

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Course dates

Starting date: Upon request

Guaranteed

Type: Self-paced

Course duration: 40 hours

Language: en

Price without VAT: 285 EUR

Register

Starting date: Upon request

Guaranteed

Type: Self-paced

Course duration: 40 hours

Language: en

Price without VAT: 75 EUR

Register

Starting
date
Place
Type Course
duration
Language Price without VAT
G Upon request Self-paced 40 hours en 285 EUR Register
G Upon request Self-paced 40 hours en 75 EUR Register
G Guaranteed course

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Course description

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.

Course structure

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

Prerequisites

  • Basic awareness of AI concepts; no prior technical skills required.
  • Willingness to explore unconventional problem-solving approaches within AI and Quantum contexts.
  • Openness to critically engaging with ethical considerations surrounding AI in quantum practices

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