Environmental Memory and Optimal Measurement Timing in Quantum First Detection: Data and Code

This record provides the numerical data, Python source code, generated figures, and exact rational certificate supporting the study “Environmental Memory and Optimal Measurement Timing in Quantum First Detection.” The calculations examine how survival-conditioned environmental information changes the measurement schedule that minimizes the mean first recorded detection time.The included models cover persistent binary detuning, random telegraph noise, Ornstein–Uhlenbeck noise, and a Markovian-dephasing control. The archive contains finite-grid Bellman results, convergence and boundary audits, pinned Python dependencies, a data dictionary, SHA-256 checksums, and step-by-step reproduction instructions.For the persistent binary-detuning model, the finest numerical grid gives an approximately 17.38% reduction relative to the best fixed-period schedule. An independent exact-rational certificate proves that an explicit two-stage schedule strictly outperforms every fixed-period schedule.This record contains only the supporting data, code, generated figures, and documentation. The article manuscript is not included.