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 โHow 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 ฮฒ and ฮณ 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
- โQAOA implementation for graph optimization
- โClassical-quantum hybrid optimization loop
- โNoise characterization and error mitigation
- โScaling analysis (n qubits vs problem size)
- โVisualization of quantum circuit and results
๐ก 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.
๐ What you'll learn
Quantum computing fundamentals, quantum circuit design, optimization theory, experimental benchmarking methodology, and scientific writing.