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| Format: | Recurso digital |
| Language: | English |
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2026
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| Online Access: | https://doi.org/10.5281/zenodo.19729926 |
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| author | Baladi, Samir |
| author_facet | Baladi, Samir |
| 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> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19729926 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | PHOTON-Q: Neural Wavefront Intelligence for Phase-Coherent Quantum-Optical Systems Baladi, Samir Physics Quantum physics Artificial intelligence Optics & Photonics Computational Modeling Quantum optics photonic ai physics-informed ai quantum coherence neural wave propagation helmholtz equation phase coherence decoherence control quantum information optical systems piai entropy lab computational physics deep learning physics quantum engineering Samir Baladi Ronin Institute Rite of Renaissance <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> |
| title | PHOTON-Q: Neural Wavefront Intelligence for Phase-Coherent Quantum-Optical Systems |
| topic | Physics Quantum physics Artificial intelligence Optics & Photonics Computational Modeling Quantum optics photonic ai physics-informed ai quantum coherence neural wave propagation helmholtz equation phase coherence decoherence control quantum information optical systems piai entropy lab computational physics deep learning physics quantum engineering Samir Baladi Ronin Institute Rite of Renaissance |
| url | https://doi.org/10.5281/zenodo.19729926 |