PHOTON-Q

📖 Overview

Quantum-Optical Efficiency Index (QOEI)

"Light is not just for seeing; it is for computing. PHOTON-Q: Mastering the Phase." — Samir Baladi, April 2026

PHOTON-Q introduces the first physics-informed AI framework for quantitative characterization and prediction of quantum-optical coherence in high-noise photonic environments — the Quantum-Optical Efficiency Index (QOEI). Built on three mathematically rigorous constructs spanning Neural Helmholtz Prediction, Phase Coherence Tensor tracking, and adaptive Phase-Locking Algorithm.

94.7%

Mean QOEI

6-regime cross-validation

8.7×

Coherence Extension

vs uncontrolled baseline

100 μs

Look-ahead Horizon

Predictive phase-locking

6

Optical Regimes

Photonic to Atmospheric

QOEI

Quantum-Optical Efficiency Index

// QOEI Composite Formula (Equation 5 from paper) QOEI = [I(ρ_in; ρ_out) - S(ρ_out||ρ_in)] / I_max ∈ [0, 1] // AI Correction with Optical/Thermal/Mechanical Bias QOEI_adj = σ(QOEI_raw + β_opt + β_therm + β_mech) // Python implementation from photon_q import QOEIParameters, compute_qoei params = QOEIParameters( nhp=0.88, pct=0.92, pla=0.85 ) result = compute_qoei(params, regime='photonic_crystal')

3 Constructs

Three Physics-Informed Constructs

Construct	Description	Domain
NHP	Neural Helmholtz Predictor	Wave propagation · Learned permittivity ε_r(r,θ)
PCT	Phase Coherence Tensor	Multi-mode coherence · Hermitian state-tracking
PLA	Phase-Locking Algorithm	Model-predictive control · 100 μs horizon

AI Architecture

Physics-Informed Neural Network + Neural ODE

// PINN penalty layer constraints // • Helmholtz compliance: ∇²E + k₀²·ε_r·E = F_AI // • Energy conservation: ∮|E(r)|² dS ≤ P_incident // • Density matrix validity: Tr(ρ)=1, ρ=ρ†, ρ ≥ 0 // Python implementation from photon_q import PhotonQ model = PhotonQ.load_pretrained("photon_q_v1.0.0") result = model.predict(optical_spectrum)

Validation Scope

Six Optical Regimes

97.3%

Photonic Crystal Cavity (R1)

Q=1.2e6 · 4.2K · 9 platforms

94.1%

Free-Space Channel (R2)

10 km · 1550 nm · 8 platforms

95.8%

Fiber Bragg Grating (R3)

50 GHz · 293K · 7 platforms

92.4%

Kerr Waveguide (R4)

n₂=2.5e-20 · 5 mm · 6 platforms

91.7%

Atmospheric Link (R5)

C_n²=1e-14 · 5 km · 5 platforms

96.2%

Silicon Photonics (R6)

SOI · 8 rings · 4 platforms

📦 Installation

Quick setup

# Clone repository git clone https://github.com/gitdeeper11/PHOTON-Q.git cd PHOTON-Q # Install package pip install -e . # Run analysis python bin/compute_qoei.py --channel test # Verify installation python -c "from photon_q import __version__; print(__version__)"

🔧 API Reference

Python interface

QOEIParameters

Three physics-informed construct container

from photon_q import QOEIParameters params = QOEIParameters( nhp=0.88, pct=0.92, pla=0.85 )

compute_qoei

QOEI computation with regime-specific normalization

from photon_q import compute_qoei result = compute_qoei(params, regime='photonic_crystal') print(result.value) # η_Q value print(result.status) # EXCELLENT/GOOD/MODERATE/CRITICAL/COLLAPSE

PhotonQ

Main framework entry point for coherence analysis

from photon_q import PhotonQ model = PhotonQ.load_pretrained("photon_q_v1.0.0") result = model.predict(optical_spectrum) print(result.qoei) # η_Q value print(result.coherence_trace) # Coherence trace

🧩 Core Modules

PHOTON-Q architecture

core/

3 Constructs

NHP, PCT, QOEI, PLA

wave/

Wave

Helmholtz solver, Kerr correction

coherence/

Coherence

Density matrix, Lindblad solver

models/

AI Models

SIREN, LSTM, MPC, PINN

sensors/

Sensors

Temperature, vibration, EM

monitoring/

Monitor

Real-time coherence tracking

👤 Author

Principal investigator

🔆

Samir Baladi

Interdisciplinary AI Researcher — Neural Optics & Quantum-Optical Intelligence Division

Ronin Institute / Rite of Renaissance

Samir Baladi is an independent researcher affiliated with the Ronin Institute, developing the Rite of Renaissance interdisciplinary research program. PHOTON-Q is a physics-informed AI framework for quantum-optical coherence control, integrating Neural Helmholtz prediction, Phase Coherence Tensor tracking, phase-locking MPC, and PINN architecture.

No conflicts of interest declared. All code and data are open-source under MIT License.

📧 gitdeeper@gmail.com 🔗 ORCID: 0009-0003-8903-0029 🐙 GitHub 🦊 GitLab

📝 Citation

How to cite

@software{baladi2026photonq, author = {Samir Baladi}, title = {PHOTON-Q: Neural Wavefront Intelligence for Phase-Coherent Quantum-Optical Systems}, year = {2026}, version = {1.0.0}, publisher = {Zenodo}, doi = {10.5281/zenodo.19729926}, url = {https://doi.org/10.5281/zenodo.19729926}, note = {Physics-Informed AI Framework for Quantum-Optical Coherence} }

"Light is not just for seeing; it is for computing. PHOTON-Q: Mastering the Phase. Quantum-optical coherence in high-noise photonic systems is not an inevitable physical ceiling — it is a predictable, multi-parameter dynamical process that can be measured, predicted, and controlled with 94.7% efficiency."