In order to eliminate the disturbance noise in the process of gas coal rupture signal acquisition, a quantum optimization noise reduction model for gas coal rupture signal based on Improved quantum swarm algorithm (IQPSO) optimized variational mode decomposition (VMD) was proposed. In view of the fact that VMD is limited by the number of decompositions and the selection of penalty parameters, which affects the noise reduction effect, the IQPSO algorithm is used to optimize the optimization process of VMD parameters, and the decision weight coefficient and adaptive control factor are introduced into the QPSO algorithm to improve the particle decision adaptability and parameter search ability of the algorithm. The VMD algorithm with parameter optimization is used to decompose the rupture signal of gas-containing coal, the effective correlation coefficient of each signal component is calculated to identify the critical point of noise, and the wavelet transform is used to process the high-frequency noise and reconstruct the remaining components to obtain the denoised gas-containing coal rupture signal. The noise reduction model is compared with the EMD, VMD, PSO-VMD, SSA-VMD, GWO-VMD models through the simulation signal and field measured signal. The experimental results show that the signal-to-noise ratio of the proposed model is increased by more than 20%, the root mean square error is reduced to less than 0.03, and the energy proportion is more than 90%, which is better than other noise reduction models, and the adaptability and decomposition efficiency are strong, which can effectively retain the local characteristics of the signal and have a better noise reduction effect on complex signals in the field.