Summary: In order to improve efficiency, stability and robustness of minimum attribute reduction in decision tables, the operators of the quantum evolutionary algorithm are designed based on the outstanding characteristics of the cloud model on the process of transforming a qualitative concept to a set of quantitative numerical values, and a novel minimum attribute reduction enhancing algorithm based on quantum cloud model evolution (QCMEARE) is proposed. First, quantum gene cloud is used to encode the evolutionary population, and reversible cloud mode based on that attribute entropy weight is designed to adaptively adjust the quantum revolving gate, so the scope of the search space can be adaptively controlled under the guidance of qualitative knowledge. Secondly, both the quantum cloud mutation and quantum cloud entanglement operators are used to avoid trapping in local optimization and converging prematurely, so as to obtain the optimization attribute reduction set. The experimental results show that the proposed algorithm can achieve high efficiency, accuracy and stability of minimum attribute reduction.