A method for compressing computer-generated holograms (CGHs) using genetic algorithm optimized quantum-inspired neural network is proposed. Genetic algorithm is a global optimization algorithm, which can provide better initial weights for the quantum-inspired neural network. The global optimization ability of genetic algorithm is combined with the local optimization ability of the quantum-inspired neural network enables the network to achieve better convergence effects. Under different compression ratios, CGHs are compressed by the genetic algorithm optimized quantum-inspired neural network and the quantum-inspired neural network respectively, and Fresnel transform technology is used to reconstruct the decompressed CGHs. The experimental results show that the genetic algorithm optimized quantuminspired neural network can obtain better quality reconstructed images than the quantum-inspired neural network while using fewer learning iterations.
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