This talk explores the emerging interface of quantum computation and machine learning. I will introduce how physics-informed generative models for quantum states provide powerful tools for high-dimensional quantum simulations, enabling new advances in high-energy physics, quantum materials, and quantum information. I will also highlight recent efforts to develop ML and robotics methods inspired by physical and quantum principles, including generative models grounded in physical processes, quantum-inspired neural solvers for PDEs, and many-body-based approaches for optimizing multi-legged robot locomotion.