Attribute reduction is a key studying point of rough set theory.It has been proven that computing minimal reduction of decision tables is a NP-hard problem.An efficient attribute reduction algorithm based on the quantum frog-leaping co-evolution is proposed.A dynamic multi-cluster frog structure is designed

individuals are represented by multi-state qubits.The self-adaptive adjustment of quantum rotation angle

quantum mutation and quantum entanglement strategies are applied to accelerate the convergence.Cooperative searching information of different clusters during attribute reduction is shared by adopting the bidirectional co-evolutionary method.Experiments on some benchmark problems indicate the proposed algorithm has outstanding ability to balance the global exploitation and local exploration on condition of the good convergence

and results on some UCI data sets validate it is more competitive on the attribute reduction accuracy and efficiency

compared to the traditional evolutionary algorithms.