According to the challenges related to the limited availability of quantum bits (qubits) in the era of noisy intermediate-scale quantum (NISQ), the immediate replacement of all components in existing network architectures with quantum computing devices may not be practical. As a result, implementing a hybrid quantum-classical system is regarded as one of effective strategies. In hybrid quantum-classical systems, quantum computing devices can be used for computation-intensive applications, such as massive scheduling in dynamic environments. Furthermore, one of most popular network applications is advanced social media services such as metaverse. Accordingly, this paper proposes an advanced multi-metaverse dynamic streaming algorithm in hybrid quantum-classical systems. For this purpose, the proposed algorithm consists of three stages. For the first stage, three-dimensional (3D) point cloud data gathering should be conducted using spatially scheduled observing devices from physical-spaces for constructing virtual multiple meta-spaces in metaverse server. This is for massive scheduling over dynamic situations, i.e., quantum multi-agent reinforcement learning-based scheduling is utilized for scheduling dimension reduction into a logarithmic-scale. For the second stage, a temporal low-delay metaverse server’s processor scheduler is designed for region-popularity-aware multiple virtual meta-spaces rendering contents allocation via modified bin-packing with hard real-time constraints. Lastly, a novel dynamic dynamic streaming algorithm is proposed for high-quality, differentiated, and stabilized meta-spaces rendering contents delivery to individual users via Lyapunov optimization theory. Our performance evaluation results verify that the proposed spatio-temporal algorithm outperforms benchmarks in various aspects over hybrid quantum-classical systems.