Responsible Quantum Adoption in Distributed Human-AI Systems: A Role-Separated Benchmark

Scientific advances create new possibilities for collective action, but their value depends on how knowledge, costs, and oversight are organized. Quantum entanglement makes this problem concrete: stronger correlations between separated agents need not remain useful once infrastructure and review are considered. Drawing on the 2022 physics laureates’ work on entanglement and economics laureates Hayek’s dispersed knowledge, Coase’s transaction costs, and Ostrom’s institutional governance, we ask when quantum capability improves distributed human–AI coordination beyond classical alternatives under comparable safeguards. We introduce Quantum-Assisted Coordination and Trust (Q-CoTrust), a reproducible simulation framework with three stylized settings, separating correlation capability, finite noisy resources, and classical certification, fallback, and review. Across 30 paired seeds in the prespecified setting with private evidence, joint payoffs, and restricted communication, the full system improves task value net of specified costs (normalized utility) by $+0.076$ over a backbone without these controls, but the matched classical comparison is $-0.003$ [$-0.009,+0.003$]. Finite noisy resources reduce the value of ideal correlations; retrained price and review-quality studies reveal positive, inconclusive, and negative margins. A simulated robot meeting task likewise favors a timely classical bit over ideal Bell correlations. The contribution connects physical possibility to economic comparison and institutional design, enabling researchers and decision-makers to distinguish a coordination opportunity from a case for purchasing infrastructure. Beginning with synthetic tasks, the framework opens a sequenced program of richer coordination experiments, measured quantum networks and robot interfaces, and participatory organizational studies to establish when technical gains survive real costs and how errors, review access, and benefits are distributed.