Quantum Algorithms for Optimization, Finance & Energy

I work on quantum and quantum-inspired algorithms for structured optimization problems where constraints, decomposition, and benchmark design are as important as the raw objective value. This includes financial networks, cardinality-constrained binary optimization, power-system unit commitment, and quantum-assisted industrial optimization.

Current directions include:

• quantum stochastic walks for portfolio construction and financial graph optimization;
• Grover-based algorithms for fixed-cardinality binary optimization;
• hybrid quantum–classical decomposition for unit commitment on near-term photonic processors;
• QUBO formulation, benchmarking, and noise-aware algorithm evaluation;
• modular frameworks where learning algorithms orchestrate rather than replace strong heuristics.

Selected outputs: npj Unconventional Computing 2026, arXiv:2603.14744, IEEE QCE/QAI 2025.