By Benjamin Lienhard

Realizing the promise of practical quantum information processing requires adept management of the intricacies of interfacing with quantum system, particularly in navigating the complexities of information extraction during system characterization, calibration, and correction. This endeavor demands a delicate balance: the resource consumption must be sufficiently low and yet swift enough for feedback and periodic recalibration.

While theoretical models offer valuable insights into the overarching structures of quantum control landscapes, they may fall short of fully capturing the nuances of real-world quantum systems. Conversely, comprehensive system characterization can yield precise numerical models, but this approach often proves cumbersome. In contrast, model-free learning control, though resource-intensive, presents a promising data-driven calibration technique.

This research endeavors to tackle the inherent challenges of complete quantum processor characterization and calibration by probing the boundaries and capabilities of inherently and engineered robust control and calibration algorithms.