Overview Three Constructs QOEI Scale Regimes Quick Setup Live Dashboard GitLab

Quantum-Optical Efficiency Index (QOEI)

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.

GitHub Repository Live Dashboard DOI: 10.5281/zenodo.19729926

94.7%

Mean QOEI

6-regime cross-validation

8.7×

Coherence Extension

vs uncontrolled baseline

100 μs

Look-ahead Horizon

Predictive phase-locking

λ/12

NHP Resolution

Sub-wavelength permittivity

The Three PHOTON-Q Constructs

NHP

Neural Helmholtz Predictor · Learned Permittivity ε_r(r,θ)

∇²E(r) + k₀²·ε_r(r,θ)·E(r) = F_AI(r, ∇E, θ) — Non-linear wave propagation with SIREN-4L architecture

PCT

Phase Coherence Tensor · Hermitian State-Tracking

C(t) = Σᵢⱼ αᵢ*·αⱼ·exp(-Γ_θ·|i-j|·Δt) — Multi-mode phase relationship tracking

QOEI

Quantum-Optical Efficiency Index · Unified Metric

η_Q = [I(ρ_in; ρ_out) - S(ρ_out||ρ_in)] / I_max — Bridging wave optics and quantum information

Quantum-Optical Efficiency Index (QOEI) Formula

η_Q = [I(ρ_in; ρ_out) - S(ρ_out||ρ_in)] / I_max ∈ [0, 1]

            I(ρ_in; ρ_out): Quantum mutual information

            S(ρ_out||ρ_in): Von Neumann relative entropy

            I_max: Channel capacity (Holevo bound)

Python Interface

            
from photon_q import PhotonQ

from photon_q.environments import PhotonicCrystalEnvironment

pq = PhotonQ.load_pretrained("photon_q_v1.0.0")

env = PhotonicCrystalEnvironment(

    cavity_mode="TE_00",

    q_factor=1.2e6,

    temperature_k=4.2

)

result = pq.compute_qoei(

    optical_input="cavity_sweep.h5",

    environment=env

)

print(f"η_Q = {result.qoei:.4f} [{result.status}]")

QOEI Alert Levels

η_Q > 0.95
EXCELLENT

0.90–0.95
GOOD

0.80–0.90
MODERATE

0.65–0.80
CRITICAL

< 0.65
COLLAPSE

EXCELLENT: Standard coherence monitoring

GOOD: Periodic phase calibration review

MODERATE: Phase-locking retuning required

CRITICAL: Emergency coherence recovery

COLLAPSE: Immediate channel shutdown

Six Optical Validation 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-Nonlinear Waveguide

R4 · n₂=2.5e-20 · 5 mm · 6 platforms

91.7%

Atmospheric Turbulence

R5 · C_n²=1e-14 · 5 km · 5 platforms

96.2%

Silicon Photonics

R6 · SOI · 8 rings · 4 platforms

Quick Setup

            
# Clone repository

git clone https://gitlab.com/gitdeeper11/PHOTON-Q.git

cd PHOTON-Q

# Install package

pip install -e .

# Run analysis

python bin/compute_qoei.py --channel test --verbose

# Verify installation

python -c "from photon_q import __version__; print(__version__)"

Physics-Informed Neural Network + Neural ODE

            
# PINN penalty layer constraints (from paper)

# • 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 PhotonQPredictor

predictor = PhotonQPredictor()

result = predictor.predict(optical_spectrum, current_params)

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}

}