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| Main Author: | |
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| Format: | Recurso digital |
| Language: | English |
| Published: |
Zenodo
2026
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| Subjects: | |
| Online Access: | https://doi.org/10.5281/zenodo.19729926 |
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Table of Contents:
- <p>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.</p> <p>The system introduces three core constructs:</p> <p>Neural Helmholtz Predictor (NHP) for adaptive wave propagation modeling</p> <p>Phase Coherence Tensor (PCT) for multi-mode coherence tracking and control</p> <p>Quantum-Optical Efficiency Index (QOEI) for unified performance evaluation</p> <p>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.</p> <p>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.</p> <p>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.</p>