Prediction of Imaging Quality based on Hybrid Quantum-Classical Neural Network Model

Optical imaging systems are widely used in daily life. Due to long-term exposure to the environment, the life of the camera will be affected. This paper analyzes the gray scale value change of the imaging quality of the optical imaging system during the gas corrosion process, and uses the gray scale value to define the pictures taken by the camera after aging. Learning the imaging quality of the camera through the hybrid quantum-classical neural network model can predict the imaging quality of the optical system well and give early warning to the aging system.