Abstract: Intelligent mine ventilation, as a crucial component of coal mine digitalization, has been faced with three major challenges for a long time: Inaccurate and slow environmental sensing in underground production sites leading to unreliable production status information; inefficient and imprecise ventilation network calculations causing safety management decisions deviating from the reality; and slow and costly disaster prediction simulations resulting in delayed forecasting. These issues hinder effective guidance for on-site operations. Quantum computing, with its advantages such as parallel exponential acceleration, offers a novel breakthrough path to address these core challenges of “inaccurate measurement, slow computation, and time-consuming simulation”. The strengths of quantum computing in model fidelity, global optimization and computational acceleration, and quantum computing power has been analyzed. And, for the first time, a mapping framework for quantum measurement, quantum algorithms, and quantum simulation have been established to tackle the “inaccurate measurement” of underlying data, the “slow computation” of network solutions, and the “time-consuming simulation” of disaster prediction in intelligent mine ventilation. as well as catastrophic events. Based on a quantum microservices architecture, a preliminary framework for a cloud-edge-end collaborative intelligent mine ventilation service platform has been established, which simultaneously examines current technical bottlenecks in quantum computing development, highlighting practical challenges for its engineering application in mine ventilation: Maintaining quantum states amid environmental interference, deploying complex systems in harsh environments, verifying quantum measurement outcomes, mapping multi-parameter quantum states, achieving robust quantum algorithms, and reducing high economic costs. A hybrid quantum algorithm combining the Quantum Newton’s Method (QCGA) and Variational Quantum Linear Solver (VQLS) was applied to mine ventilation network calculations, achieving speedups of 11.9 and 12.8 respectively. This demonstrates the feasibility of quantum computing acceleration for solving problems in intelligent mine ventilation, and provides theoretical guidance for leveraging quantum computing to drive technological transformation in intelligent mine ventilation and advances smart mine construction.