Murphy Niu's long-term research goal is to develop quantum computing paradigms in regard to how we program, control, characterize, measure, and error correct a large-scale quantum computer without paying the steep price of digitization towards real-world impacts. Niu applies cutting-edge deep reinforcement learning and generative models to quantum control, quantum circuit compilation, and quantum system learning using some of the largest quantum computers based on superconducting qubits. Her recent research focus on developing scalable analog quantum control and algorithms for emerging quantum architectures with superconducting, ion trap, photonic, and neutral atom qubits.