IE 458 sits at the intersection of operations research and quantum computing, asking how combinatorial problems you already know how to attack classically can be recast as Ising or QUBO instances and handed to a quantum device. Expect to spend the term reformulating problems, building circuits in Qiskit, running annealing jobs through Ocean SDK and OpenJij, and benchmarking the results against classical baselines, with three homeworks, a midterm, a final, and a project that ties it together. The course assumes you are comfortable with optimization and linear algebra and gives no quantum background for free; it is the natural bridge from IE's optimization track into NISQ-era algorithms like QAOA and quantum annealing, where the honest question is not "is it faster" but "under what hardware limits is it competitive at all."
→ STARS müfredatı (resmi syllabus)
Bilkent'in resmî syllabus'ünden. Sağdaki etiket o çıktının hangi değerlendirmeyle ölçüldüğünü söylüyor.
İlk dosyayı sen atarsan: not, slayt, geçmiş sınav, çözüm, cheat-sheet, ne varsa. defter ekibi öğrenci paylaşımlarından bu dersin notlarını yazar. Drive linki / PDF / ZIP, hepsi olur.
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