Quantum Robust Optimal Control with Q-PRONTO

Quantum optimal control (QOC) has been implemented extensively in quantum computing and quantum sensing. High-fidelity qubit gate obtained via QOC saturates quantum speed limit (QSL), and sensing scheme generated by QOC approaches the ultimate Heisenberg Limit (HL). However, almost all QOC sequences suffer from the imperfection in real world experiments: they are extremely sensitive to the noise introduced by inputs or environments. In this talk, I will describe quantum robust optimal control (QROC), a method to design the control law that not only possesses noise resilience, but also maintains high performance. I will demonstrate such control law can be numerically computed by Q-PRONTO, which is an efficient QOC solver that can achieve quadratic convergence.