ThesisResearchSystems 10-14 months
Quantum Computing Algorithms for Optimization Problems
Implementing and benchmarking quantum optimization algorithms (QAOA, VQE) on combinatorial problems — comparing quantum advantage against classical solvers on near-term quantum hardware.
PythonQiskit (IBM)PennylaneNumPyNetworkXMatplotlib
Find GitHub repos & source code →How to build it — step by step
- 1Problem Formulation: Map MAX-CUT and Traveling Salesman Problem to QUBO (Quadratic Unconstrained Binary Optimization) form
- 2QAOA Implementation: Implement p-layer QAOA circuit in Qiskit; optimize β and γ parameters with classical optimizer (COBYLA)
- 3Hardware Experiments: Run on IBM Quantum (real hardware) and simulator; compare noise impact via error mitigation techniques
- 4Benchmarking: Compare solution quality and runtime vs classical solvers: simulated annealing, branch & bound
Key features to implement
- ✓QAOA implementation for graph optimization
- ✓Classical-quantum hybrid optimization loop
- ✓Noise characterization and error mitigation
- ✓Scaling analysis (n qubits vs problem size)
- ✓Visualization of quantum circuit and results
💡 Unique twist to stand out
Explore the "quantum advantage frontier": find the smallest problem size where QAOA outperforms classical simulated annealing on current NISQ hardware — contributing empirical data to the quantum advantage debate.
🎓 What you'll learn
Quantum computing fundamentals, quantum circuit design, optimization theory, experimental benchmarking methodology, and scientific writing.