Quantum Optimisation for Transport Vulnerability Identification

This is a fresh arXiv preprint (3 days old, 0 stars/forks) applying known quantum optimization techniques (QAOA, VQE) to a specific domain problem (transport network resilience). The novelty lies in framing multi-link vulnerability identification as a quantum-amenable combinatorial optimization problem, but the core techniques are established. No evidence of working code, community adoption, or production implementation. The paper tackles a real problem (exponential scenario space in classical transport analysis) with a defensible angle (quantum speedup for combinatorial problems), but this is a prototype at best. Frontier risk is HIGH because: (1) Google, IBM, and Anthropic are actively building quantum optimization tools; (2) the problem is domain-specific but the technique is generic; (3) major cloud providers (AWS Braket, Azure Quantum) are already commoditizing quantum access, making this a natural extension of their platforms; (4) the paper is pre-publication, so reproduction and competition could happen immediately post-release. The contribution is theoretically sound but lacks the ecosystem, validation, or switching costs needed to defend against platform integration. A frontier lab could trivially add transport network benchmarks to their quantum SDK and subsume this work.