  High-fidelity control of qubits requires precisely tuned control parameters.
Typically, these parameters are found through a series of bootstrapped
calibration experiments which successively acquire more accurate information
about a physical qubit. However, optimal parameters are typically different
between devices and can also drift in time, which begets the need for an
efficient calibration strategy. Here, we introduce a framework to understand
the relationship between calibrations as a directed graph. With this approach,
calibration is reduced to a graph traversal problem that is automatable and
extensible.
