Planar weibull quantum circuit genetic algorithm with strong search ability and its implementation

Abstract

Genetic Algorithm (GA) is a widely used search method, and Quantum Genetic Algorithm (QGA) has been pointed out for several years. While common QGA only refers to the concept of quantum mechanics, and still uses mathematical representations of quantum states to simulate quantum entanglement and superposition, it can’t be realized on a quantum computer. This paper presents an implementable quantum circuit for a genetic algorithm, named as Planar-Weibull Quantum Circuit Genetic Algorithm (PW-QCGA), employing the Weibull function and excitation function through planar matrix coding. It is not only a self-adaptive algorithm that adjusts parameters dynamically, but it can also be executed on quantum computers by constructing parameterized quantum circuits. Experiments on three typical applications show that PW-QCGA not only has obvious advantages on convergence accuracy, search ability, convergence speed, and robustness, but also has higher fidelity on a quantum computer. We hope this work can bring inspiration to other quantum intelligent algorithms.