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Course Outline

Overview of Quantum Mechanics Fundamentals

  • Core tenets of quantum theory
  • Quantum states and qubit architecture
  • Principles of superposition and entanglement

Foundations of Quantum Computing

  • Construction of quantum circuits and operational gates
  • Procedures for qubit measurement and manipulation
  • Introductory concepts in quantum algorithm design

Quantum Algorithm Development

  • Survey of established quantum algorithms
  • Implementation and utility of the quantum Fourier transform
  • Application of Grover's algorithm for search optimization

Intersection of Quantum Computing, Artificial Intelligence, and Machine Learning

  • Algorithms for quantum-enhanced machine learning
  • Architecture of quantum neural networks
  • Strategic opportunities for Quantum AI in public sector operations

Operational Challenges and Future Directions for Quantum AI

  • Technical barriers to Quantum AI adoption
  • Ethical frameworks and societal implications
  • Emerging trends and research priorities for Quantum AI

Practical Laboratory Exercises

  • Execution of quantum algorithms via Qiskit or comparable frameworks
  • Development of foundational quantum machine learning models
  • Team-based initiative to propose innovative Quantum AI applications for government use

Conclusion and Subsequent Actions

Requirements

  • Foundational knowledge of linear algebra and quantum mechanics principles
  • Proficiency in Python programming for operational tasks

Audience

  • Artificial intelligence practitioners engaged in public sector applications
  • AI researchers contributing to government solutions
 14 Hours

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