Quantum-Enhanced Travel Procurement: Hybrid Quantum–Classical Optimization for Enterprise Travel Management

Corporate travel procurement is a multi-objective, constraint-dense decision domain spanning strategic supplier portfolio selection, travel policy design, and operational disruption response. Classical methods—mixed-integer programming (MIP), constraint programming, metaheuristics, and machine learning—deliver strong results but face scaling and responsiveness challenges as procurement objectives broaden to include compliance, traveler experience, sustainability, and resilience. Quantum optimization methods, particularly quantum annealing and gate-based variational algorithms (e.g., QAOA), have been proposed for combinatorial problems with binary decisions and complex interaction terms. Yet empirical evidence across optimization domains often shows classical solvers match or exceed quantum approaches on meaningful instance sets, motivating a hybrid quantum–classical posture rather than replacement.
This paper formalizes quantum-enhanced travel procurement as a hypothesis-driven, hybrid decision-support approach in which quantum routines contribute to solution search, diversification, or time-to-decision for selected combinatorial cores. We present canonical procurement formulations, show explicit mappings to QUBO/Ising models, and propose hybrid architectures for supplier portfolio design, airline share allocation under commitments, policy parameter tuning, and disruption re-accommodation. To strengthen accessibility and rigor, we provide (i) a worked QUBO example with explicit coefficients and variable counts; (ii) an operational benchmark protocol with representative instance sizes, solver baselines, runtime assumptions, and statistical reporting; and (iii) an enterprise governance view including post-quantum cryptography readiness across vendor integrations.