Abstract

Quantum-inspired approaches are rapidly advancing Artificial Intelligence by leveraging quantum mechanics principles. This includes enhancing reinforcement learning agents for complex environments and improving generative adversarial networks for image synthesis. These methods also boost diagnostic model performance in medical imaging and optimize solutions for complex AI problems. Further applications extend to efficient data processing in image classification, robust financial time series prediction, and accelerated linear equation solving. Additionally, quantum-inspired algorithms refine feature selection and improve multi-class classification, collectively offering significant gains in efficiency, accuracy, and scalability across diverse AI domains.