Quantum Approximate Optimization Algorithm (QAOA) for MaxCut
This capstone explores QAOA for solving the MaxCut problem. Participants will implement QAOA, optimize graph partitioning, and compare quantum vs. classical performance using real-world datasets and quantum computing frameworks.
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Course Curriculum & Resources
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Fundamentals of QAOA and combinatorial optimization
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Understanding the MaxCut problem and its applications
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Encoding MaxCut as a quantum optimization problem
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Implementing QAOA using Qiskit and fine-tuning parameters
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Comparing QAOA vs. classical algorithms for MaxCut
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Evaluating execution on quantum simulators and hardware
Student Ratings & Reviews
Instructor Info
- Instructor Code: ABM07
- Specialty: Cloud, Data Science & GenAI Expert
- Experience: Worked with Microsoft, JO Morgan Chase, Deloitte Consulting in the USA
- Education: Holds an MS in Data Analytics Engineering from George Mason University, USA, and an MTech in Biomedical Engineering from IIT Bombay.
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