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Course Outline

Foundations of Quantum Mechanics

  • Core principles of quantum mechanics
  • Quantum states and the concept of qubits
  • Superposition and entanglement phenomena

Basics of Quantum Computing

  • Quantum circuits and gate operations
  • Measurement techniques and qubit control
  • Introduction to core quantum algorithms

Advanced Quantum Algorithms

  • Overview of key quantum algorithmic approaches
  • The Quantum Fourier Transform and its use cases
  • Grover's algorithm for database search optimization

Quantum AI and Machine Learning

  • Algorithms specifically designed for quantum machine learning
  • Architecture of quantum neural networks
  • Exploring the potential use cases of Quantum AI

Challenges and Future Trajectories

  • Technical hurdles in the deployment of Quantum AI
  • Ethical frameworks and broader societal impact
  • Emerging trends and future research avenues in Quantum AI

Practical Lab Project

  • Simulating quantum algorithms using Qiskit or equivalent quantum computing frameworks
  • Building a basic quantum machine learning model
  • Collaborative group work to conceptualize an innovative Quantum AI application

Requirements

  • Foundational knowledge of linear algebra and quantum mechanics.
  • Proficiency in Python programming.

Target Audience

  • AI professionals.
  • AI researchers.
 14 Hours

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