Quantum Inspired ISRU Lattice: Adaptive Control Demonstration (Qiskit v1.0)

This dataset validates a modular ISRU lattice control layer using local Qiskit simulations. Three mission critical subsystems are modeled as six qubit QAOA optimization problems with binary control policies under nominal and stressed conditions. The subsystems are water recovery, acid and corrosion control, and thermal regulation. Simulations run with the Qiskit Aer Sampler at 1,000 shots per run and a grid over gamma and beta with 13 to 17 steps. Outputs include bitstring distributions, energies, and ranked candidate policies. Included contents code folder with Python 3.11 scripts for each subsystem and small analysis helpers data folder with scenario configuration JSON files for the two acid and corrosion cases used in the study results folder with JSON outputs for all runs for water, acid, and thermal under nominal and stressed conditions README.txt with environment information, run instructions, and a file by file guide requirements.txt with exact versions including Qiskit 1.2.4, Qiskit Aer 0.14 to below 0.16, NumPy 1.26.4, pandas 2.2.2, Matplotlib 3.9.2 How to reproduce Create and activate a Python 3.11 virtual environment and run pip install -r requirements.txt Run the scripts in the code folder to regenerate outputs. The Aer Sampler produces candidate policies with probabilities and energies that match the provided results Use the inspect utilities to tabulate and review the top candidates Why this mattersOff world ISRU systems must adapt as conditions shift. This study shows that a lightweight quantum inspired control layer based on QAOA can minimize energy in relaxed regimes and degrade gracefully under stress, supporting resource aware and fault tolerant operations.