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 Duration 14 hours

Course Outline

Introduction to Quantum Mechanics

  • Core principles of quantum mechanics
  • Quantum states and the concept of qubits
  • Exploring superposition and entanglement

Basics of Quantum Computing

  • Quantum circuits and gate structures
  • Measurement techniques and qubit control
  • Fundamental overview of quantum algorithms

Quantum Algorithms

  • General overview of quantum algorithmic strategies
  • The Quantum Fourier transform and its use cases
  • Grover's algorithm for efficient database searching

Quantum AI and Machine Learning

  • Algorithms tailored for quantum machine learning
  • Structure and function of quantum neural networks
  • Real-world application scenarios for Quantum AI

Challenges and the Future of Quantum AI

  • Current technical limitations in Quantum AI
  • Ethical considerations and broader societal impact
  • Emerging trends and future research pathways in Quantum AI

Lab Project

  • Simulation of quantum algorithms using Qiskit or comparable quantum computing frameworks
  • Creation of a basic quantum machine learning model
  • Group collaboration to design a novel Quantum AI application proposal

Requirements

  • A solid grasp of linear algebra and the basics of quantum mechanics
  • Proficiency in Python programming

Target Audience

  • AI professionals
  • AI researchers

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