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Course Outline

Foundations of Quantum Mechanics

  • Core concepts of quantum mechanics
  • Exploration of quantum states and qubits
  • Understanding superposition and entanglement

Essentials of Quantum Computing

  • Construction of quantum circuits and gates
  • Processes of quantum measurement and qubit control
  • Introduction to algorithmic approaches in quantum computing

Exploring Quantum Algorithms

  • General overview of quantum algorithms
  • The Quantum Fourier transform and its utility
  • Grover's algorithm applied to database searching

Quantum AI and Machine Learning Integration

  • Algorithms tailored for quantum machine learning
  • Structure and function of quantum neural networks
  • Industry-specific applications of Quantum AI

Challenges and Future Trajectories of Quantum AI

  • Current technical hurdles in Quantum AI development
  • Ethical frameworks and societal consequences
  • Emerging trends and future research paths

Practical Lab Project

  • Simulating quantum algorithms using frameworks like Qiskit
  • Constructing a rudimentary quantum machine learning model
  • Collaborative group work to design a novel Quantum AI application

Requirements

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

Intended Audience

  • AI Professionals
  • AI Researchers
 14 Hours

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