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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