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Course Outline

Introduction to Quantum Mechanics

  • Core principles of quantum mechanics
  • Quantum states and qubits
  • Concepts of superposition and entanglement

Quantum Computing Basics

  • Quantum circuits and gate operations
  • Measurement techniques and qubit manipulation
  • Foundational overview of quantum algorithms

Quantum Algorithms

  • Survey of major quantum algorithms
  • The Quantum Fourier Transform and its uses
  • Grover's algorithm for optimized database search

Quantum AI and Machine Learning

  • Algorithms for quantum machine learning
  • Architectures of quantum neural networks
  • Exploring prospective applications in industry

Challenges and Future of Quantum AI

  • Technical hurdles in quantum AI development
  • Ethical implications and broader societal effects
  • Emerging trends and future research pathways

Practical Lab Project

  • Simulating quantum algorithms using frameworks like Qiskit
  • Building a basic quantum machine learning model
  • Collaborative group project to propose an innovative Quantum AI solution

Requirements

  • A solid grounding in linear algebra and quantum mechanics.
  • Proficiency in Python programming.

Target Audience

  • AI professionals.
  • AI researchers.
 14 Hours

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Provisional Upcoming Courses (Require 5+ participants)

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