Abstract: In dynamic heterogeneous networks, massive heterogeneous terminals face a severe communication bottleneck when performing distributed collaborative learning: the frequent exchange of large-scale model parameters among terminals incurs heavy communication overhead. To address this issue, this paper proposes QStar, a quantum-native federated aggregation algorithm for heterogeneous terminals. Unlike existing quantum federated learning schemes that convert quantum model updates into classical bit strings before transmission, QStar performs the transmission and aggregation of model updates within the quantum domain: clients encode local parameter updates into quantum superposition states so that a single quantum state transmission carries diverse candidate update strategies, and the server performs projective measurements guided by historical optimization trajectories to complete aggregation directly within the quantum domain. This mechanism makes the cost of carrying multiple candidate updates grow logarithmically rather than linearly with the number of candidates, and embeds candidate comparison and selection into the quantum measurement itself. Extensive experiments in typical heterogeneous terminal collaboration scenarios show that, compared with quantum federated learning baselines based on classical parameter transmission, QStar improves convergence stability and attains higher classification accuracy than parameter-quantization-based schemes on tasks with more classes and more complex features, providing a feasible technical pathway for collaborative learning of massive heterogeneous terminals in dynamic heterogeneous networks.