Machine learning for quantum control and information processing in trapped-ion systems

Ion trap systems are among the most important physical platforms for quantum information processing. With joint efforts from research groups worldwide, trap architectures have advanced toward miniaturization, integration, and improved optical accessibility, while markedly enhancing multi-ion trapping and high-precision control. As a core methodology in artificial intelligence, machine learning provides powerful tools for modeling and optimizing quantum systems, and is playing an increasingly vital role. This review outlines the evolution of ion trap architectures and the fundamental principles of ion confinement, and critically assesses the applications and future prospects of traditional and quantum machine learning for system-level optimization and quantum information processing in trapped-ion platforms.