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Course Outline

Foundations of Quantum Mechanics

  • Core tenets 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 quantum algorithm design

Advanced Quantum Algorithms

  • Survey of key quantum algorithms
  • The Quantum Fourier Transform and its utility
  • Grover’s algorithm applied to database search

Quantum AI and Machine Learning Integration

  • Algorithms for quantum machine learning
  • Architecture of quantum neural networks
  • Exploring the practical utility of Quantum AI

Challenges and the Trajectory of Quantum AI

  • Technical hurdles in advancing Quantum AI
  • Ethical dimensions and societal impact
  • Emerging trends and research pathways in the field

Practical Lab Project

  • Simulating quantum algorithms via Qiskit or comparable frameworks
  • Building a fundamental quantum machine learning model
  • Collaborative group work to propose innovative Quantum AI applications

Requirements

  • A foundational grasp of linear algebra and quantum mechanics
  • Working knowledge of Python programming

Target Audience

  • AI professionals
  • AI researchers
 14 Hours

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