Quantum genetic algorithm and its application in magnetotelluric data inversion
量子遗传算法在大地电磁反演中的应用

Based on quantum mechanics, the quantum genetic algorithm (QGA) encodes with qubit instead of binary codes of classical genetic algorithms and makes directional updating with quantum rotation gates to replace the procedures of selection, crossover and mutation in genetic algorithms, therefore the algorithm possesses the great capabilities of internal parallel computing and quantum tunneling effect, to speed up the searching speed and improve the convergence rate greatly in searching the global optimization. In this paper, the author proposes a realizing scheme for geophysical inversion problem with nonlinear and multi-minimum properties, and test many synthetic models and real data to study the reliability in MT inversion. The computing efficiency of quantum genetic algorithm shows that it is a more stable and effective nonlinear inversion method with global convergence than traditional genetic algorithm.