PHOTON-Q: Neural Wavefront Intelligence for Phase-Coherent Quantum-Optical Systems

PHOTON-Q is a Physics-Informed Artificial Intelligence (PIAI) framework designed to model, predict, and preserve quantum coherence in photonic systems under high-noise environmental conditions. The system introduces three core constructs: Neural Helmholtz Predictor (NHP) for adaptive wave propagation modeling Phase Coherence Tensor (PCT) for multi-mode coherence tracking and control Quantum-Optical Efficiency Index (QOEI) for unified performance evaluation PHOTON-Q integrates classical electromagnetic theory with quantum information constraints, enabling real-time correction of decoherence effects such as thermal drift, scattering, and nonlinear optical perturbations. The framework has been validated across multiple optical regimes including photonic crystal cavities, fiber systems, free-space channels, and silicon photonics, achieving up to 94.7% coherence retention and significant extension of coherence time. This project is part of the broader EntropyLab research ecosystem and follows an open-science approach with full reproducibility via code, datasets, and archived releases.