MAQCal: A Bounded Sequential Decision Workflow for Quantum Readout Calibration

Quantum readout calibration repeatedly requires experimenters to locate a resonator frequency and select a readout power, with sweep ranges, resolutions, and stopping decisions often configured from device-specific experience. We present MAQCal, a bounded sequential decision workflow for these two coupled stages. Stage-specific planners propose typed sweep actions from measurement feedback. A deterministic runtime enforces stage order, numerical constraints, and iteration limits, and selects the returned estimates. We provide the planner interfaces and execution rules to support reproducibility. We also study a weighted $k$-nearest-neighbor (kNN) prior for frequency-search initialization using simulated chip records. On a simulation-driven dataset of 2006 chip designs and three synthetic noise families, the mean of MAQCal's relative SNR gains over the strongest fixed preset in each of 15 reported settings is 13.7%, with setting-specific gains from 4.4% to 23.4%. Stage-wise and scan-budget experiments show consistent improvements in the evaluated frequency and power decisions. MAQCal automates repeated sweep choices and improves readout quality over the tested fixed configurations in simulation under matched downstream processing.