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Course Outline
Foundations of Quantum Mechanics
- Core principles 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 core quantum algorithms
Advanced Quantum Algorithms
- Overview of key quantum algorithmic approaches
- The Quantum Fourier Transform and its use cases
- Grover's algorithm for database search optimization
Quantum AI and Machine Learning
- Algorithms specifically designed for quantum machine learning
- Architecture of quantum neural networks
- Exploring the potential use cases of Quantum AI
Challenges and Future Trajectories
- Technical hurdles in the deployment of Quantum AI
- Ethical frameworks and broader societal impact
- Emerging trends and future research avenues in Quantum AI
Practical Lab Project
- Simulating quantum algorithms using Qiskit or equivalent quantum computing frameworks
- Building a basic quantum machine learning model
- Collaborative group work to conceptualize an innovative Quantum AI application
Requirements
- Foundational knowledge of linear algebra and quantum mechanics.
- Proficiency in Python programming.
Target Audience
- AI professionals.
- AI researchers.
14 Hours