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Quantum optimization is increasingly being applied to real-world combinatorial problems. This talk presents an experience-driven overview of how such methods are modeled and deployed across multiple domains, including transportation, healthcare, agriculture, and resource allocation.

In our work, these problems have been formulated using QUBO-based representations and implemented on a range of computational platforms, including quantum annealers, digital annealers, and simulated annealing systems on both quantum and quantum-inspired environments.

To illustrate the workflow in practice, a bike sharing system rebalancing problem is used as a representative case study. The talk highlights practical observations from real deployments and discusses current limitations and open challenges from a practitioner’s perspective.