Efforts to scale-up quantum computation have reached a point where the principal limiting factor is not the number of qubits, but the entangling gate infidelity. However, the highly detailed system characterization required to understand the underlying error sources is an arduous process and impractical with increasing chip size. Open-loop optimal control techniques allow for the improvement of gates but are limited by the models they are based on. To solve these problems we investigate novel ways to perform optimal control, calibration and characterization or superconducting QPUs and other quantum devices. To that end we employ quantum control theory, optimization theory and machine learning techniques. And we build software which takes these ideas from theory to practice.