Predicting Quantum Decoherence from Spacetime Structure: A Machine Learning Test of the Fractal Physis Model

We present the first machine-learned, predictive model of quantum decoherence derived from local structural features of spacetime. Extending the Fractal Physis framework, this work demonstrates that quantum decoherence is not a fundamentally stochastic or observer-driven event, but can be forecast directly from curvature, entropy, field stress, and material phase. Using a dataset of 20,000 synthetic spacetime zones encoded with ten physical features, we train a dense neural network to predict the local decoherence susceptibility field with 92% accuracy and an AUC of 0.994. This field governs the decoherence strength within Fractal Physis, enabling real-time prediction of interference loss with no measurement context. This release includes all training code, full feature mapping, model checkpoints, visibility simulation functions, and evaluation plots. It provides the first open-source, structure-driven decoherence predictor - fully testable, falsifiable, and laboratory-relevant. These results mark a transition from interpretive decoherence to predictive physics: decoherence is not random, it is structural, measurable, and now machine-learned. Please read file: Predicting_Quantum_Decoherence.pdf for full theoretical and methodological context. For foundational background, see: Fractal_Physis_Decoherence.pdf. v1.0 release includes complete code, documentation, and reproducibility pipeline. For questions or collaborations, contact: [email protected]