All ProjectsContact Dr. Hossain
Quantum ComputingHybrid AlgorithmsNetwork Optimization
Hybrid Quantum-Classical Computing for Next-Generation Networks
Many of the hardest problems in next-generation networking, among them resource allocation, routing under uncertainty, and slice admission, are combinatorial and grow faster than classical solvers can follow. Near-term quantum hardware cannot run these workloads end to end, but it can take the part classical methods handle worst. This project studies how to split a network optimization problem between the two, which subproblems justify a quantum solver at all, and what a hybrid pipeline must do to stay useful under the noise and qubit limits of current devices.
Research Objectives
- Identify the network optimization subproblems where a quantum solver offers real advantage over a strong classical heuristic
- Build hybrid pipelines that decompose large problems into quantum-sized cores with classical pre- and post-processing
- Characterize how gate noise and limited coherence degrade solution quality at realistic problem sizes
- Benchmark hybrid pipelines against tuned classical baselines rather than naive ones
Methods & Techniques
- QAOA and variational quantum eigensolvers for combinatorial subproblems
- Quantum annealing on QUBO formulations of resource allocation
- Classical decomposition, including Benders and column generation, to size subproblems for available hardware
- Noise-aware circuit design and error mitigation across simulators and cloud quantum backends
Interested in this research?
Get in touch to discuss collaboration or graduate opportunities.