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Course Outline

Introduction to Quantum Mechanics

  • Foundational principles of quantum mechanics
  • Concepts of quantum states and qubits
  • Phenomena of superposition and entanglement

Foundations of Quantum Computing

  • Design of quantum circuits and gates
  • Techniques for quantum measurement and qubit control
  • Overview of foundational quantum algorithms

Quantum Algorithms

  • Broad survey of quantum algorithmic approaches
  • Application of the Quantum Fourier transform
  • Use of Grover's algorithm in database search scenarios

Quantum AI and Machine Learning

  • Integration of quantum machine learning techniques
  • Architecture of quantum neural networks
  • Exploration of potential Quantum AI use cases

Challenges and the Future of Quantum AI

  • Addressing technical limitations in Quantum AI
  • Navigating ethical considerations and broader societal impact
  • Forecasting future trends and research avenues

Practical Lab Project

  • Simulation of quantum algorithms using Qiskit or equivalent frameworks
  • Construction of a basic quantum machine learning model
  • Collaborative group work to propose innovative Quantum AI applications

Requirements

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

Target Audience

  • AI professionals
  • AI researchers
 14 Hours

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