Abstract: As shallow coal resources become increasingly depleted, deep coal mining is key to ensuring national energy security. In deep mining, the spatiotemporal correlation characteristics of coal and gas outbursts and coal–rock–gas coupled dynamic disasters become more complex, with significantly enhanced chain effects and coupling behaviors, which makes mine ventilation and safety control more difficult. Based on an analysis of the advantages of quantum computing in information representation, information storage, and computational paradigms, as well as the challenges faced by complex mine ventilation and safety control in deep coal mining such as multiphysics coupling solutions and high-dimensional computation, this study explores the potential advantages of a full-chain technical system empowered by quantum computing for complex mine ventilation and safety control from microscopic mechanisms to macroscopic systems. These advantages include ① revealing microscopic disaster-inducing mechanisms of multiphysics coupling through quantum simulation, ② rapidly solving high-dimensional models through quantum parallel computation, ③ efficiently addressing combinatorial optimization problems in mine ventilation and safety systems through quantum combinatorial optimization algorithms while accelerating model training and deeply mining and integrating multi-source heterogeneous precursor information through quantum machine learning, ④ building an intelligent computing platform for mine ventilation and safety systems through the integration of artificial intelligence and quantum computing. The challenges of engineering applications of quantum computing are analyzed, including short quantum coherence time, susceptibility to environmental interference, limitations in the applicability of quantum algorithms, and constraints of quantum hardware facilities. Future research directions are identified as exploring feasibility through multi-instance verification, the superiority of engineering applications, and adaptability across multiple scenarios. This study aims to provide a theoretical reference for promoting the development of intelligent mine ventilation and safety systems toward the integration of artificial intelligence, quantum computing, and quantum artificial intelligence.