Physics-Informed Reinforcement Learning for Multi-Scale Optimization of Majorana Nanowire Device Yield

Physics-informed reinforcement learning for multi-scale optimization of Majorana nanowire device yield. This dataset contains synthetic nanowire configurations, quantum transport simulation outputs, machine learning training data, reinforcement learning optimization results, and publication-ready figures supporting a predicted 74.1% fabrication yield improvement over 47% baseline. All simulations are physics-constrained and fully reproducible. The dataset is intended for research in quantum device engineering, topological quantum computing, and AI-assisted nanofabrication optimization. Includes: Synthetic nanowire dataset and defect maps Quantum transport feature datasets Reinforcement learning optimization outputs Validation tables and figures Reproducibility documentation