This graduate elective bridges combinatorial optimization with the physics of quantum computing, focusing on how problems IE students already know how to model classically can be recast as Ising or QUBO formulations and attacked on actual quantum hardware. You'll work through three homeworks, a midterm and final, and a course project where you implement algorithms like quantum annealing, QAOA, and Grover search using Qiskit, D-Wave's Ocean SDK, and OpenJij on simulators and real devices. It sits at the edge of what's currently practical, so a big part of the course is honestly benchmarking quantum approaches against classical solvers and understanding why NISQ-era limitations still matter.
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Course Learning Outcomes: Course Learning Outcome Assessment Explain the fundamental principles of quantum computation effectively applied to solving optimization problems. Midterm Exam Final Exam Homework Assignment Transform classical combinatorial optimization problems into quantum-compatible formulations, such as Ising and QUBO models. Midterm Exam Course Project Homework Assignment Demonstrate a strong understanding of leading NISQ-era algorithms, specifically Quantum Annealing and QAOA. Mi