Comparisons of quantum rotation gates in quantum-inspired evolutionary algorithms

As a novel evolutionary algorithm, a quantum-inspired evolutionary algorithm (QIEA) is attracting increasing attention, due to its good global search capability and rapid convergence. A quantum rotation gate (QR-gate) is the key operator in a QIEA. In the literature, there are many versions of QR-gates. How to evaluate and choose a QR-gate is very worth discussing. This paper focuses on a comparison and analysis on six QR-gates. The performances of QIEAs with the six QR-gates are tested on a practical problem, image sparse decomposition. Experimental results show that the QR-gate5 is superior to other five versions of QR-gates in terms of the quality of solutions and computing time.