⟨ PHOTON-Q ⟩ v1.0.0

Neural Wavefront Intelligence for Phase-Coherent Quantum-Optical Systems

Light is not just for seeing; it is for computing. PHOTON-Q: Mastering the Phase.

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A Physics-Informed AI Framework for Neural Wavefront Propagation,
Phase Coherence Tensor Tracking, and Quantum-Optical Efficiency Prediction
in High-Noise Photonic and Quantum Communication Environments

Submitted to Entropy (MDPI), ISSN 1099-4300 — April 2026

🌐 Website · 📊 Dashboard · 📚 Docs · 📑 Reports · 🔖 Zenodo

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📋 Table of Contents

• Overview
• Key Results
• The Three PHOTON-Q Constructs
• QOEI Performance Levels
• Project Structure
• Installation
• Quick Start
• Validation Regimes
• Case Studies
• Modules Reference
• Configuration
• Dashboard
• AI Architecture
• Contributing
• Citation
• Author
• Funding
• License

---

🌊 Overview

PHOTON-Q is an open-source, Physics-Informed Artificial Intelligence (PIAI) framework engineered to model light-matter interaction dynamics and predict photonic entanglement states under high-noise environmental conditions. It integrates three mathematically rigorous constructs — the Neural Helmholtz Predictor (NHP), the Phase Coherence Tensor (PCT), and the Quantum-Optical Efficiency Index (QOEI) — validated across six canonical optical regimes spanning the complete operational envelope of current and near-term quantum photonic technology.

The framework addresses a fundamental gap in quantum photonics: no existing control system simultaneously models non-linear wave propagation, tracks multi-mode phase coherence with predictive decoherence compensation, and provides a regime-independent efficiency scalar. PHOTON-Q achieves this unification and delivers a 94.7% mean QOEI at signal-to-noise ratios as low as 8 dB, with an 8.7× coherence time extension over uncontrolled baselines — the first physics-constrained AI system to demonstrate cross-regime generalization with less than 4.2% performance degradation on unseen optical environments.

🔬 Core hypothesis: Quantum decoherence in photonic systems is not an inevitable physical ceiling — it is a predictable, multi-parameter dynamical process. Phase relationships between optical modes encode environmental histories in their coherence tensor off-diagonals; the Neural Helmholtz Predictor resolves sub-wavelength permittivity inhomogeneities that no deterministic model can capture; and the adaptive Phase-Locking Algorithm governs coherence retention with a collective predictive logic that no single-parameter correction can achieve. PHOTON-Q makes this decoherence process measurable, predictable, and controllable in real time.

PHOTON-Q targets the enabling technology for:

• Quantum key distribution (QKD) — coherence preservation across atmospheric and fiber channels for secure communication
• Photonic quantum computing — phase-stable gate operations in silicon photonic integrated circuits
• Quantum sensing and metrology — sub-wavelength displacement measurement with decoherence-corrected interferometry
• Free-space quantum networking — entanglement distribution over turbulent atmospheric links
• On-chip quantum photonics — fabrication-disorder compensation in silicon and InP waveguide platforms
• Quantum memory and repeaters — coherence extension in rare-earth-doped crystal optical memories

---

📊 Key Results

Metric	Value
Mean QOEI (η_Q) across all regimes	94.7% at SNR = 8 dB
Coherence Time Extension (T2)	8.7× over uncontrolled baseline
Cross-Regime Generalization Drop	< 4.2% (zero retraining)
NHP Spatial Resolution	λ/12 (sub-wavelength permittivity)
Peak η_Q (Photonic Crystal Cavity)	97.3%
Min η_Q (Atmospheric Turbulence)	91.7%
Phase-Locking Prediction Horizon	100 μs look-ahead
T2 Extension (Photonic Crystal)	1.1 μs → 9.8 μs (vs. 12.3 μs phonon limit)
Training Compute	2,400 GPU-hours (4× A100)
Validation Regimes	6 platforms · 18 sensor stations · 2 temperature extremes

---

🔬 The Three PHOTON-Q Constructs

#	Construct	Symbol	Physical Domain	Role
1	Neural Helmholtz Predictor	NHP	Wave propagation / Non-linear optics	Learns spatially varying permittivity ε_r(r,θ) and corrects Kerr, Raman, and XPM effects
2	Phase Coherence Tensor	PCT	Quantum coherence dynamics	Tracks multi-mode phase relationships; drives adaptive Phase-Locking Algorithm
3	Quantum-Optical Efficiency Index	QOEI	Quantum information theory	Unified scalar metric bridging classical wave optics and quantum channel capacity

Core Physical Equations

# Neural Helmholtz Predictor (NHP) — non-linear wave propagation with learned permittivity
∇²E(r) + k₀² · ε_r(r,θ) · E(r) = F_AI(r, ∇E, θ)

# k₀ = ω/c: free-space wave number
# ε_r(r,θ): spatially varying permittivity learned by SIREN-4L network
# F_AI: non-linear AI correction field (Kerr effect, two-photon absorption, stimulated Raman)

# NHP Training Loss — composite physics-constrained objective
L_NHP(θ) = λ₁·L_pde + λ₂·L_bc + λ₃·L_phys + λ₄·L_kerr

# λᵢ: adaptive loss weights (NTK-rebalanced every 100 epochs)
# L_phys: energy conservation — prevents hallucinated energy artifacts
# L_kerr: Kerr-effect regularization for intensity-dependent index

# Phase Coherence Tensor (PCT) — Hermitian multi-mode coherence tracking
C(t) = Σᵢⱼ αᵢ*(t,θ) · αⱼ(t,θ) · exp(-Γ_θ(t)·|i-j|·Δt)

# αᵢ(t,θ): neurally optimized mode amplitude (LSTM-128 architecture)
# Γ_θ(t): learned decoherence rate — predicted 100 μs ahead from environmental sensors
# Δt: coherence sampling interval

# Phase-Locking Objective — model-predictive coherence maximization
max_{φ_corr} ∫₀ᵀ Tr[C(t, φ_corr)] dt   subject to |φ_corr(t)| ≤ φ_max

# T: coherence horizon  |  φ_max: electro-optic modulator saturation

# Quantum-Optical Efficiency Index (QOEI) — unified information-theoretic metric
η_Q = [I(ρ_in; ρ_out) - S(ρ_out||ρ_in)] / I_max   ∈ [0, 1]

# I(·;·): quantum mutual information
# S(·||·): von Neumann relative entropy (entropic overhead of PLA intervention)
# I_max: channel capacity upper bound (Holevo bound)

---

🚦 QOEI Performance Levels

η_Q Range	Status	Indicator	Management Action
> 0.95	EXCELLENT	🟢	Standard coherence monitoring — no intervention required
0.90 – 0.95	GOOD	🟡	Periodic phase calibration review
0.80 – 0.90	MODERATE	🟠	Phase-locking parameter retuning required
0.65 – 0.80	CRITICAL	🔴	Emergency coherence recovery — check environment sensors
< 0.65	COLLAPSE	⚫	Immediate optical channel shutdown and full recalibration

Construct-Level Thresholds

Construct	Symbol	EXCELLENT	GOOD	MODERATE	CRITICAL	COLLAPSE
QOEI	η_Q	> 0.95	0.90–0.95	0.80–0.90	0.65–0.80	< 0.65
NHP Residual	L_pde	< 1×10⁻⁴	1–5×10⁻⁴	5–20×10⁻⁴	20–100×10⁻⁴	> 100×10⁻⁴
Coherence Trace	Tr(C)	> 0.95	0.85–0.95	0.70–0.85	0.50–0.70	< 0.50
Decoherence Rate	Γ_θ	< 10⁶ s⁻¹	10⁶–10⁷ s⁻¹	10⁷–10⁸ s⁻¹	10⁸–10⁹ s⁻¹	> 10⁹ s⁻¹
Phase Correction	φ_corr	< 0.1 φ_max	0.1–0.3 φ_max	0.3–0.6 φ_max	0.6–0.9 φ_max	> 0.9 φ_max
T2 Extension Factor	T2/T2⁰	> 8×	5–8×	3–5×	1.5–3×	< 1.5×

---

🗂️ Project Structure

photon-q/
│
├── README.md                              # This file
├── LICENSE                                # MIT License
├── CHANGELOG.md                           # Version history
├── CONTRIBUTING.md                        # Contribution guidelines
├── CODE_OF_CONDUCT.md                     # Community standards
├── SECURITY.md                            # Vulnerability reporting
├── pyproject.toml                         # Build system configuration
├── setup.cfg                              # Package metadata
├── requirements.txt                       # Core dependencies
├── requirements-dev.txt                   # Development dependencies
├── .gitlab-ci.yml                         # GitLab CI/CD pipeline
├── .gitignore                             # Git ignore rules
├── .pre-commit-config.yaml                # Pre-commit hooks
│
├── photon_q/                              # ⚡ Core Python package
│   ├── __init__.py
│   ├── version.py                         # Version metadata
│   │
│   ├── core/                              # 🌊 Quantum-optical physics engine
│   │   ├── __init__.py
│   │   ├── psi_dynamics_tracker.py        # PsiDynamicsTracker — central state object
│   │   ├── nhp.py                         # Neural Helmholtz Predictor
│   │   ├── pct.py                         # Phase Coherence Tensor
│   │   ├── qoei.py                        # Quantum-Optical Efficiency Index
│   │   ├── phase_locking.py               # Phase-Locking Algorithm (PLA)
│   │   └── composite.py                   # System-level composite evaluator
│   │
│   ├── wave/                              # 🔬 Wave propagation engine
│   │   ├── __init__.py
│   │   ├── helmholtz_solver.py            # Analytical Helmholtz PDE solver
│   │   ├── siren_nhp.py                   # SIREN-4L neural permittivity network
│   │   ├── kerr_corrector.py              # Kerr effect non-linear correction module
│   │   ├── raman_model.py                 # Stimulated Raman scattering model
│   │   ├── xpm_coupler.py                 # Cross-phase modulation handler
│   │   ├── energy_conservator.py          # Energy conservation constraint enforcer
│   │   └── wavefront_sampler.py           # Spatial collocation point sampler
│   │
│   ├── coherence/                         # 🌀 Coherence dynamics module
│   │   ├── __init__.py
│   │   ├── density_matrix.py              # Quantum density matrix algebra (ρ)
│   │   ├── lindblad_solver.py             # Lindblad master equation solver
│   │   ├── decoherence_lstm.py            # LSTM-128 decoherence rate predictor
│   │   ├── phase_locking_mpc.py           # Model-predictive phase-locking controller
│   │   ├── coherence_tensor.py            # Hermitian PCT construction and update
│   │   └── t2_tracker.py                  # T2 dephasing time measurement module
│   │
│   ├── models/                            # 🤖 AI model architecture
│   │   ├── __init__.py
│   │   ├── photon_q_engine.py             # Main PHOTON-Q inference engine
│   │   ├── siren.py                       # SIREN network implementation
│   │   ├── lstm_decoherence.py            # Decoherence prediction LSTM
│   │   ├── mpc_controller.py              # Phase-locking MPC solver
│   │   ├── domain_adapter.py              # Domain-adaptive batch normalization
│   │   └── curriculum_trainer.py          # Three-phase curriculum training manager
│   │
│   ├── environments/                      # 🌐 Optical regime configurations
│   │   ├── __init__.py
│   │   ├── photonic_crystal.py            # Photonic crystal cavity (R1)
│   │   ├── free_space_channel.py          # Free-space entanglement channel (R2)
│   │   ├── fiber_bragg.py                 # Fiber Bragg grating (R3)
│   │   ├── kerr_waveguide.py              # Kerr-nonlinear waveguide (R4)
│   │   ├── atmospheric_link.py            # Atmospheric turbulence link (R5)
│   │   ├── silicon_photonics.py           # On-chip silicon photonics (R6)
│   │   └── environment_registry.py        # Dynamic environment loader
│   │
│   ├── sensors/                           # 📡 Environmental sensor interface
│   │   ├── __init__.py
│   │   ├── temperature_reader.py          # Thermal gradient sensor interface
│   │   ├── vibration_psd.py               # Mechanical vibration PSD reader
│   │   ├── em_noise_monitor.py            # EM background noise monitor
│   │   ├── atmosphere_turbulence.py       # Kolmogorov turbulence parameter reader
│   │   └── sensor_registry.py             # Multi-sensor aggregation layer
│   │
│   ├── monitoring/                        # 📡 Coherence health monitoring
│   │   ├── __init__.py
│   │   ├── coherence_monitor.py           # Real-time η_Q monitoring engine
│   │   ├── alert_engine.py                # QOEI alert level engine
│   │   ├── decoherence_predictor.py       # 100 μs look-ahead decoherence alarm
│   │   ├── intervention_planner.py        # Physics-attributed recovery planner
│   │   └── health_reporter.py             # Automated optical health PDF reports
│   │
│   ├── quantum/                           # ⚛️ Quantum information module
│   │   ├── __init__.py
│   │   ├── mutual_information.py          # Quantum mutual information I(ρ_in; ρ_out)
│   │   ├── von_neumann_entropy.py         # Von Neumann relative entropy S(ρ||σ)
│   │   ├── holevo_bound.py                # Holevo channel capacity upper bound
│   │   ├── tomography_proxy.py            # State tomography proxy metrics
│   │   └── density_matrix_ops.py          # Positivity / Hermiticity / trace constraints
│   │
│   ├── data/                              # 💾 Data pipeline
│   │   ├── __init__.py
│   │   ├── optical_loader.py              # Optical measurement data loader
│   │   ├── eis_spectrum_parser.py         # EIS / optical spectrum parser
│   │   ├── sensor_time_series.py          # Environmental time-series parser
│   │   ├── synthetic_generator.py         # Analytical Helmholtz synthetic data generator
│   │   └── normalizer.py                  # Cross-regime descriptor normalization
│   │
│   ├── visualization/                     # 📈 Visualization module
│   │   ├── __init__.py
│   │   ├── qoei_dashboard.py              # Live QOEI monitoring dashboard
│   │   ├── wavefront_renderer.py          # 3D wavefront field renderer
│   │   ├── coherence_plotter.py           # Coherence tensor evolution plotter
│   │   ├── phase_map.py                   # Phase correction field visualizer
│   │   └── regime_comparator.py           # Cross-regime QOEI comparison plots
│   │
│   └── utils/                             # 🛠️ Utility functions
│       ├── __init__.py
│       ├── config.py                      # Configuration loader (YAML / TOML)
│       ├── logger.py                      # Structured logging (structlog)
│       ├── validators.py                  # Input validation and schema checks
│       ├── units.py                       # Optical / quantum unit conversion
│       ├── constants.py                   # Physical constants (ħ, c, k_B, ε₀)
│       └── io.py                          # File I/O utilities (HDF5, JSON, CSV)
│
├── configs/                               # ⚙️ Configuration files
│   ├── default.yaml                       # Default PHOTON-Q configuration
│   ├── photonic_crystal.yaml              # Photonic crystal cavity preset (R1)
│   ├── free_space_channel.yaml            # Free-space channel preset (R2)
│   ├── fiber_bragg.yaml                   # Fiber Bragg grating preset (R3)
│   ├── kerr_waveguide.yaml                # Kerr waveguide preset (R4)
│   ├── atmospheric_link.yaml              # Atmospheric turbulence preset (R5)
│   └── silicon_photonics.yaml             # Silicon photonics preset (R6)
│
├── data/                                  # 📦 Data assets
│   ├── reference/
│   │   ├── regime_thresholds.csv          # Per-regime QOEI threshold tables
│   │   ├── nhp_weights_init.json          # SIREN weight initialization reference
│   │   ├── decoherence_atlas.h5           # 18-station decoherence rate atlas
│   │   └── permittivity_atlas.json        # 6-regime permittivity baseline reference
│   │
│   ├── validation/
│   │   ├── held_out_regimes.h5            # R5–R6 held-out validation data
│   │   ├── t2_benchmarks.csv              # T2 dephasing time benchmarks
│   │   └── qoei_confirmations.csv         # Laboratory η_Q confirmations
│   │
│   └── examples/
│       ├── photonic_crystal_sweep.h5      # Sample R1 cavity coherence sweep
│       ├── atmospheric_channel.csv        # Sample R5 atmospheric turbulence log
│       └── silicon_chip_scan.json         # Sample R6 on-chip disorder scan
│
├── models/                                # 🧠 Pre-trained model weights
│   ├── photon_q_v1.0.0/
│   │   ├── nhp_siren.pt                   # SIREN-4L NHP model weights
│   │   ├── lstm_decoherence.pt            # LSTM decoherence predictor weights
│   │   ├── mpc_controller.json            # Phase-locking MPC parameters
│   │   └── ensemble_config.json           # Full system configuration
│   │
│   └── regime_specific/
│       ├── photonic_crystal_v1.pt         # R1 fine-tuned NHP weights
│       ├── fiber_bragg_v1.pt              # R3 fine-tuned NHP weights
│       └── silicon_photonics_v1.pt        # R6 fine-tuned NHP weights
│
├── notebooks/                             # 📓 Jupyter notebooks
│   ├── 01_quick_start.ipynb               # Getting started walkthrough
│   ├── 02_nhp_training.ipynb              # Neural Helmholtz Predictor tutorial
│   ├── 03_phase_coherence_tensor.ipynb    # PCT construction and evolution
│   ├── 04_phase_locking_mpc.ipynb         # Phase-Locking Algorithm deep dive
│   ├── 05_qoei_computation.ipynb          # QOEI metric computation tutorial
│   ├── 06_atmospheric_channel.ipynb       # Free-space turbulence link example
│   ├── 07_silicon_photonics.ipynb         # On-chip disorder compensation example
│   └── 08_cross_regime_transfer.ipynb     # Cross-regime generalization benchmark
│
├── scripts/                               # 🖥️ Utility scripts
│   ├── compute_qoei.py                    # Standalone QOEI computation script
│   ├── monitor_channel.py                 # Real-time channel monitoring launcher
│   ├── run_nhp_training.py                # NHP curriculum training launcher
│   ├── export_report.py                   # PDF optical health report exporter
│   ├── benchmark.py                       # Framework performance benchmarking
│   ├── daily_report.py                    # Daily coherence report generator
│   └── update_regime_thresholds.py        # Regime threshold recalibration tool
│
├── reports/                               # 📋 Generated reports
│   ├── daily/                             # Daily coherence monitoring reports
│   └── archive/                           # Archived optical health reports
│
├── tests/                                 # 🧪 Test suite
│   ├── __init__.py
│   ├── unit/
│   │   ├── test_nhp.py                    # NHP wave propagation unit tests
│   │   ├── test_pct.py                    # PCT coherence tensor unit tests
│   │   ├── test_qoei.py                   # QOEI metric computation unit tests
│   │   ├── test_phase_locking.py          # PLA controller unit tests
│   │   ├── test_lindblad.py               # Lindblad solver correctness tests
│   │   ├── test_density_matrix.py         # Density matrix constraint tests
│   │   └── test_siren.py                  # SIREN network activation tests
│   ├── integration/
│   │   ├── test_photonic_crystal.py       # R1 end-to-end integration test
│   │   ├── test_atmospheric_link.py       # R5 turbulence regime integration test
│   │   ├── test_silicon_photonics.py      # R6 on-chip integration test
│   │   └── test_full_pipeline.py          # Full system pipeline integration test
│   ├── regression/
│   │   ├── test_known_systems.py          # Regression against T2 benchmarks
│   │   └── test_held_out_regimes.py       # Validation against held-out R5–R6
│   └── conftest.py                        # Shared pytest fixtures
│
├── docs/                                  # 📚 Documentation
│   ├── index.md
│   ├── installation.md
│   ├── quick_start.md
│   ├── theory/
│   │   ├── nhp_derivation.md              # Neural Helmholtz Predictor derivation
│   │   ├── pct_formulation.md             # Phase Coherence Tensor theory
│   │   ├── qoei_metric.md                 # QOEI physical interpretation
│   │   ├── phase_locking_mpc.md           # Phase-Locking Algorithm formulation
│   │   └── decoherence_physics.md         # Lindblad decoherence theory
│   ├── api/
│   │   ├── core.md                        # Core construct API reference
│   │   ├── wave.md                        # Wave propagation engine API reference
│   │   ├── coherence.md                   # Coherence module API reference
│   │   ├── quantum.md                     # Quantum information API reference
│   │   └── monitoring.md                  # Health monitoring API reference
│   ├── tutorials/
│   │   ├── photonic_crystal_cavity.md     # Photonic crystal cavity tutorial
│   │   ├── free_space_qkd.md              # Free-space QKD link tutorial
│   │   ├── silicon_photonics.md           # On-chip disorder compensation tutorial
│   │   └── custom_regime.md               # Adding a new optical regime
│   └── mkdocs.yml
│
├── dashboard/                             # 🖥️ Web dashboard (Netlify)
│   ├── index.html
│   ├── dashboard.html
│   ├── results.html
│   ├── documentation.html
│   ├── assets/
│   └── netlify.toml
│
└── paper/                                 # 📄 Research manuscript
    ├── PHOTON-Q_Research_Paper.pdf        # Full research paper
    ├── figures/
    └── supplementary/

---

🛠️ Installation

Requirements

Dependency	Version	Purpose
Python	≥ 3.10	Runtime
PyTorch	≥ 2.3	Neural network backbone
JAX + Optax	≥ 0.4.25	PINN wave propagation
torchdiffeq	≥ 0.2.3	Neural-ODE coherence evolution
qutip	≥ 5.0	Lindblad master equation solving
scipy	≥ 1.11	Helmholtz PDE numerical solver
numpy	≥ 2.0	Numerical computation
cvxpy	≥ 1.4	Phase-locking MPC solver

Standard Installation

pip install photon-q-tensor

From Source (Recommended for Research)

# Clone the primary repository (GitLab)
git clone https://gitlab.com/gitdeeper11/PHOTON-Q.git
cd PHOTON-Q

# Create and activate environment
python -m venv photon_env
source photon_env/bin/activate   # Linux / macOS
# photon_env\Scripts\activate    # Windows

# Install in development mode
pip install -e ".[dev,quantum,dashboard]"

# Install pre-commit hooks
pre-commit install

Verify Installation

python -c "import photon_q; photon_q.verify()"
# Expected output:
# ✅ PHOTON-Q v1.0.0 — all systems operational
# ✅ Neural Helmholtz Predictor (SIREN-4L): LOADED
# ✅ Phase Coherence Tensor tracker: ACTIVE
# ✅ LSTM decoherence predictor: READY
# ✅ Phase-Locking MPC controller: READY
# ✅ QOEI metric engine: READY

---

⚡ Quick Start

Single Channel QOEI Computation

from photon_q import PhotonQ
from photon_q.environments import PhotonicCrystalEnvironment

# Initialize framework
pq = PhotonQ.load_pretrained("photon_q_v1.0.0")

# Define optical environment
env = PhotonicCrystalEnvironment(
    cavity_mode="TE_00",
    q_factor=1.2e6,
    temperature=4.2,           # K (cryogenic)
    phonon_bath_coupling=1e-3
)

# Compute full QOEI profile
result = pq.compute_qoei(
    optical_input="cavity_sweep.h5",
    environment=env,
    qoei_threshold=0.90,
    enforce_hermiticity=True
)

# Inspect results
print(f"QOEI (η_Q):         {result.qoei:.4f}  [{result.qoei_status}]")
print(f"Coherence Trace:    {result.coherence_trace:.4f}")
print(f"NHP Residual:       {result.nhp_residual:.2e}")
print(f"T2 Extension:       {result.t2_extension:.1f}×")
print(f"Decoherence Rate:   {result.gamma:.2e} s⁻¹")
print(f"Action:             {result.intervention_recommendation}")

Real-Time Coherence Monitoring

from photon_q import PhotonQ
from photon_q.environments import AtmosphericLinkEnvironment
from photon_q.monitoring import CoherenceMonitor
from photon_q.core import PsiDynamicsTracker

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

env = AtmosphericLinkEnvironment(
    link_distance_km=10.0,
    cn2_turbulence=1e-14,       # m^(-2/3) — moderate turbulence
    wavelength_nm=1550,
    aperture_diameter_m=0.3
)

tracker = PsiDynamicsTracker(mode_dim=64, lstm_hidden=128)

monitor = CoherenceMonitor(
    channel_id="QKD-LINK-BERLIN-01",
    environment=env,
    tracker=tracker,
    alert_threshold=0.80,
    monitoring_interval_ms=100
)

# Start real-time monitoring with look-ahead alarm
monitor.start(sensor_endpoint="http://sensor-api/optical")

Batch Regime Analysis

from photon_q.core import QOEIComputer
from photon_q.data import OpticalLoader

loader = OpticalLoader()
measurements = loader.load_batch("regime_data/", pattern="*.h5")

computer = QOEIComputer(environment="silicon_photonics")
results = computer.compute_batch(measurements)

for measurement, qoei_profile in zip(measurements, results):
    print(f"{measurement.channel_id}:  η_Q={qoei_profile.qoei:.4f}  "
          f"Tr(C)={qoei_profile.coherence_trace:.4f}  "
          f"T2_ext={qoei_profile.t2_extension:.1f}×  "
          f"Status={qoei_profile.status}  "
          f"Action={qoei_profile.intervention_recommendation}")

PsiDynamicsTracker — Direct State Evolution

from photon_q.core import PsiDynamicsTracker
import numpy as np

# Initialize tracker with 64 optical modes
tracker = PsiDynamicsTracker(mode_dim=64, lstm_hidden=128)

# Environmental observation at each timestep
env_obs = {
    'temperature_K': 293.1,
    'vibration_psd': np.array([...]),    # mechanical PSD [W/Hz]
    'em_background': 1.2e-12             # EM noise power [W]
}

# Single-step state evolution (1 ns timestep)
result = tracker.step(dt=1e-9, env_obs=env_obs)

print(f"Decoherence rate predicted: {result.gamma:.3e} s⁻¹")
print(f"Phase correction applied:   {result.phi_corr:.4f} rad")
print(f"Coherence trace:            {result.trace_c:.4f}")
print(f"η_Q this step:              {result.qoei:.4f}")

---

🔭 Validation Regimes

ID	Regime	Native τ_c	Primary Noise Mechanism	PHOTON-Q η_Q	T2 Extension
R1	Photonic Crystal Cavity	~1 μs	Phonon scattering	97.3%	1.1 → 9.8 μs
R2	Free-Space Entanglement Channel	~50 ns	Atmospheric turbulence	94.1%	50 → 430 ns
R3	Fiber Bragg Grating	~500 ns	Thermal index drift	95.8%	500 ns → 4.3 μs
R4	Kerr-Nonlinear Waveguide	~10 ns	Self-phase modulation	92.4%	10 → 87 ns
R5	Atmospheric Turbulence Link	~5 ns	Kolmogorov turbulence	91.7%	5 → 43 ns
R6	On-Chip Silicon Photonics	~200 ns	Fabrication disorder	96.2%	200 ns → 1.7 μs
—	Mean (all regimes)	~293 ns	—	94.7%	8.7×

All η_Q values reported at SNR = 8 dB. R5–R6 are held-out validation regimes (zero retraining required).

---

🔬 Case Studies

Case Study A — Photonic Crystal Cavity: Phonon-Limited Coherence Extension

System: InGaAsP photonic crystal L3 nanocavity · Q-factor: 1.2×10⁶ · Temperature: 4.2 K

PHOTON-Q's SIREN-NHP resolved the spatially varying dielectric environment of the photonic crystal with λ/12 resolution, identifying three localized phonon scattering hotspots that classical homogeneous permittivity models missed. The Phase Coherence Tensor tracked the 64-mode state with a mean coherence trace of 0.971, extending T2 from 1.1 μs to 9.8 μs — 79% of the theoretical phonon-limited ceiling of 12.3 μs. The QOEI achieved 97.3%, the highest recorded across all six regimes.

Case Study B — Atmospheric QKD Link: Kolmogorov Turbulence Compensation

System: 10 km free-space QKD link · Turbulence strength: C_n² = 1×10⁻¹⁴ m⁻²/³ · Wavelength: 1550 nm

Atmospheric turbulence induced rapid phase drift at rates reaching 8×10⁸ s⁻¹ during thermal boundary layer events. The LSTM decoherence predictor successfully anticipated these events 100 μs in advance with 89.4% accuracy, allowing the PLA controller to pre-compensate phase corrections before decoherence onset. QOEI was maintained at 91.7% — 13.8 percentage points above the best classical adaptive optics benchmark (77.9%) under identical turbulence conditions.

Case Study C — Silicon Photonics: Fabrication Disorder Correction

System: Silicon ring resonator array (8 rings) · Disorder level: Δn_eff = ±2×10⁻³ · Platform: IMEC 220 nm SOI

Manufacturing variability introduced stochastic phase errors of up to ±0.34 rad per waveguide crossing. PHOTON-Q's NHP learned the disorder profile from 200 training sweeps and suppressed the effective phase error standard deviation to ±0.031 rad — a 10.9× reduction. The Phase Coherence Tensor maintained off-diagonal coherences |C_ij| > 0.85 for the full 8-ring array across a 500 nm wavelength window, enabling wavelength-division multiplexed quantum operations without per-channel recalibration.

Case Study D — Fiber Bragg Grating: Thermal Drift Compensation

System: Fiber Bragg grating quantum memory · Thermal gradient: 0.5 K/cm · Bandwidth: 50 GHz

Thermal gradients in the fiber introduced slow drift in the Bragg resonance wavelength at rates of 12 pm/°C, causing progressive phase misalignment in stored optical pulses. The LSTM decoherence predictor tracked the thermal evolution with a prediction RMSE of 0.8 pm, enabling pre-emptive PLA corrections that maintained coherence trace above 0.94 for storage durations up to 4.3 μs — 8.6× the uncontrolled baseline of 500 ns.

---

📦 Modules Reference

Module	Key Classes	Description
photon_q.core	PsiDynamicsTracker, QOEIComputer, PhotonQ	Central state evolution and inference engine
photon_q.wave	NeuralHelmholtzPredictor, SIRENNetwork, KerrCorrector	Wave propagation with learned permittivity
photon_q.coherence	PhaseCoherenceTensor, LindbladSolver, PhaseLockingMPC	Coherence tracking and phase-locking control
photon_q.quantum	QOEIMetric, MutualInformation, HolevoBound	Quantum information theory computations
photon_q.models	PhotonQEngine, DecoherenceLSTM, CurriculumTrainer	AI architecture and training
photon_q.monitoring	CoherenceMonitor, AlertEngine, InterventionPlanner	Real-time optical health monitoring
photon_q.sensors	TemperatureReader, VibrationPSD, AtmosphereTurbulence	Environmental sensor interface layer
photon_q.visualization	QOEIDashboard, WavefrontRenderer, CoherencePlotter	Interactive visualization tools

---

⚙️ Configuration

# configs/photonic_crystal.yaml

environment:
  name: photonic_crystal
  regime_id: R1
  cavity_mode: TE_00
  q_factor: 1.2e6
  temperature_K: 4.2
  phonon_bath_coupling: 1.0e-3

wave_propagation:
  qoei_threshold: 0.90
  enforce_hermiticity: true
  enforce_energy_conservation: true
  spatial_resolution: lambda_over_12

nhp:
  architecture: siren_4l
  hidden_width: 256
  activation_frequency: 30       # ω₀ for SIREN
  collocation_points: 512
  precision: float64

coherence:
  mode_dim: 64
  lstm_hidden: 128
  prediction_horizon_us: 100
  phase_correction_max_rad: 3.14159

pct:
  update_interval_ns: 1
  coherence_threshold: 0.85
  off_diagonal_monitor: true

training:
  curriculum_phase_1_epochs: 500
  curriculum_phase_2_epochs: 1500
  curriculum_phase_3_epochs: 3000
  optimizer: adamw
  learning_rate: 3.0e-4
  weight_decay: 1.0e-5
  batch_size: 512
  loss_rebalance_interval: 100

loss_weights:
  lambda_pde: 1.0
  lambda_bc: 10.0
  lambda_phys: 5.0
  lambda_kerr: 2.0

---

📊 Dashboard

Live at photon-q.netlify.app

Panel	Description
⚡ Coherence Monitor	Real-time η_Q scores for all active optical channels and regimes
📈 QOEI Trajectory	Time-series η_Q evolution per channel with alert overlays
🌊 Wavefront Map	3D NHP wavefront field visualization colored by coherence level
🔬 Construct Profile	Per-channel NHP residual / Tr(C) / γ / φ_corr breakdown
📉 Phase Spectrum	Interactive phase coherence spectrum across optical modes
🔴 Intervention Feed	Real-time decoherence alarm with look-ahead prediction and recommended actions
⚠️ Alert Feed	Real-time QOEI alert notifications
📋 Channel Report	Exportable PDF optical coherence health report per channel

# Launch local dashboard
python -m photon_q.visualization.qoei_dashboard --port 8050
# Open: http://localhost:8050

---

🤖 AI Architecture

⟨ PHOTON-Q NEURAL ARCHITECTURE ⟩

INPUT STREAMS               MODEL LAYERS                      OUTPUT
─────────────────────────────────────────────────────────────────────
Optical field E(r)          SIREN-4L (ω₀=30)                 η_Q (QOEI)
(wavefront scan)            Neural Helmholtz Predictor        = I(ρ_in;ρ_out)/I_max
                            + Kerr / Raman correction         corrected by S(ρ||σ)

Environmental sensors       LSTM-128                          SECONDARY OUTPUTS:
(T, vibration PSD, EM)      Decoherence rate predictor        ■ Phase correction signal
                            100 μs look-ahead window            φ_corr(t) [rad]
                                                              ■ Coherence trace
Mode amplitudes α(t)        Hermitian PCT construction          Tr[C(t)]
(optical mode basis)        + MPC Phase-Locking solver        ■ Decoherence alarm
                                                                (100 μs advance)
─────────────────────────────────────────────────────────────────────
Training: R1–R4 (curriculum, 2400 GPU-hours)   Validation: R5–R6 (held-out)

Three Physical Constraints Enforced at Every Prediction Step

1. Helmholtz compliance — predicted permittivity field must satisfy the wave equation residual below L_pde < 5×10⁻⁴
2. Energy conservation — integral of |E(r)|² over any closed surface must not exceed incident power
3. Density matrix validity — ρ must remain positive-semidefinite, Hermitian, and unit-trace at all timesteps

Intervention Attribution Guide

Dominant Signal	Physical Interpretation	Recommended Action
NHP residual spike	Sub-wavelength permittivity disorder detected	Activate Kerr pre-compensation; inspect waveguide for defect sites
Γ_θ surge (LSTM)	Anticipated thermal or mechanical decoherence event	Pre-apply PLA phase correction; activate vibration isolation
Tr(C) off-diagonal collapse	Multi-mode dephasing — mode coupling breakdown	Reduce optical power; enable cross-mode phase locking
φ_corr saturation	Phase-locking bandwidth exceeded	Expand modulator bandwidth; reduce channel operating rate
η_Q entropy excess	PLA intervention entropic overhead too high	Retune MPC horizon T; reduce correction frequency
QOEI step discontinuity	Environmental sensor dropout	Switch to predicted-only mode; flag sensor for maintenance

---

🤝 Contributing

We welcome contributions from quantum physicists, photonic engineers, AI researchers, and software developers.

# 1. Fork on GitLab and clone
git clone https://gitlab.com/gitdeeper11/PHOTON-Q.git
cd PHOTON-Q

# 2. Create a feature branch
git checkout -b feature/your-feature-name

# 3. Install development dependencies
pip install -e ".[dev]"
pre-commit install

# 4. Run tests
pytest tests/unit/ tests/integration/ -v
ruff check photon_q/
mypy photon_q/

# 5. Commit with conventional commits
git commit -m "feat: add your feature description"
git push origin feature/your-feature-name

# 6. Open a Merge Request on GitLab

Priority contribution areas:

• New optical regime configurations (YAML + calibration datasets)
• Continuous-variable quantum information encoding — planned for v3.0
• Entanglement swapping across multi-node quantum networks — planned for v2.0
• Gaussian boson sampling coherence control extension
• Quantum optimal control theory integration (nanosecond gate timescales)
• ENTRO-EVO adaptive weighting integration for autonomous regime discovery
• Documentation translation (Arabic, French, German, Japanese, Chinese)
• GPU-accelerated Lindblad solver for real-time Tr(C) updates

---

📖 Citation

If you use PHOTON-Q in your research, please cite all of the following:

Research Paper

@article{Baladi2026PHOTONQ,
  title     = {PHOTON-Q: Neural Wavefront Intelligence for Phase-Coherent
               Quantum-Optical Systems — A Physics-Informed AI Framework for
               Neural Helmholtz Prediction, Phase Coherence Tensor Tracking,
               and Quantum-Optical Efficiency Index Computation in
               High-Noise Photonic Environments},
  author    = {Baladi, Samir},
  journal   = {Entropy},
  publisher = {MDPI},
  issn      = {1099-4300},
  year      = {2026},
  month     = {April},
  doi       = {10.5281/zenodo.19729926},
  url       = {https://doi.org/10.5281/zenodo.19729926}
}

Software (PyPI)

@software{Baladi2026PHOTONsoftware,
  author    = {Baladi, Samir},
  title     = {photon-q-tensor: Physics-Informed AI Framework for Quantum-Optical Coherence Control},
  version   = {1.0.0},
  year      = {2026},
  publisher = {PyPI},
  url       = {https://pypi.org/project/photon-q-tensor/1.0.0/},
  note      = {Python library for QOEI computation and phase-locking control}
}

Dataset (Zenodo)

@dataset{Baladi2026PHOTONdata,
  author    = {Baladi, Samir},
  title     = {PHOTON-Q Optical Validation Dataset:
               6 Regimes, 18 Sensor Stations, 2 Temperature Extremes},
  year      = {2026},
  publisher = {Zenodo},
  version   = {1.0.0},
  doi       = {10.5281/zenodo.19729926},
  url       = {https://doi.org/10.5281/zenodo.19729926},
  license   = {CC-BY-4.0}
}

APA (plain text)

Baladi, S. (2026). PHOTON-Q: Neural Wavefront Intelligence for Phase-Coherent
Quantum-Optical Systems. Entropy (MDPI).
https://doi.org/10.5281/zenodo.19729926

Baladi, S. (2026). photon-q-tensor (Version 1.0.0) [Python package]. PyPI.
https://pypi.org/project/photon-q-tensor/1.0.0/

Baladi, S. (2026). PHOTON-Q Optical Validation Dataset (Version 1.0.0) [Data set].
Zenodo. https://doi.org/10.5281/zenodo.19729926

---

👤 Author

Field	Details
Name	Samir Baladi
Role	Principal Investigator · Framework Design · Software Development · Analysis
Affiliation	Ronin Institute / Rite of Renaissance
Designation	Interdisciplinary AI Researcher — Neural Optics & Quantum-Optical Intelligence Division
Email	gitdeeper@gmail.com
ORCID	0009-0003-8903-0029
Phone	+1 (614) 264-2074
GitLab	gitlab.com/gitdeeper11
GitHub	github.com/gitdeeper11

PHOTON-Q is the sixth expression of the Deep Tech category within a coherent interdisciplinary research program:

Framework	Domain	Core Index
PALMA	Desert oasis ecosystem monitoring	OHI
METEORICA	Extraterrestrial geochemical systems	MGI
BIOTICA	Terrestrial ecosystem resilience	BRI
FUNGI-MYCEL	Fungal network intelligence	MNIS
MET-AL	Transition metal coordination bond stability	CBSI
PIEZO-X	Piezoelectric energy harvesting in extreme environments	PEGI
CHRONOS-AI	Temporal drift correction in high-velocity monitoring systems	TDCI
EntropyLab (E-LAB-01–05)	Thermodynamic entropy · Shannon theory · AI control	UDSF / AEW
GENESIS-X	De novo molecular design in unexplored chemical space	XFI
ION-Logic	Ion transport dynamics in electrochemical systems	LFI
PHOTON-Q	Quantum-optical coherence in high-noise photonic environments	QOEI

The methodological architecture is consistent across the full program: the three-construct physics-informed composite, PINN constraint enforcement, environment-specific threshold normalization, and adaptive AI ensemble — progressively refined from desert oasis hydrology to the quantum-optical frontier. PHOTON-Q represents the arrival of this research lineage at its most fundamental domain: the preservation of quantum information encoded in light.

---

💰 Funding

Grant	Funder	Amount
Quantum Photonics AI Initiative (NSF-PHY-2026)	National Science Foundation	$44,000
PINN HPC Allocation (TG-PHY2026-PHOTON)	XSEDE / ACCESS	$28,000
Cryogenic Optics Lab Access (QO-2026)	NIST Joint Measurement Agreement	In-kind
Independent Scholar Award	Ronin Institute	$43,000

Total: ~$115,000 + infrastructure

---

🔗 Repositories & Links

Platform	URL
🦊 GitLab (primary)	gitlab.com/gitdeeper11/PHOTON-Q
🐙 GitHub (mirror)	github.com/gitdeeper11/PHOTON-Q
🏴 Bitbucket	bitbucket.org/gitdeeper11/photon-q
🏕 Codeberg	codeberg.org/gitdeeper11/PHOTON-Q
📦 PyPI	pypi.org/project/photon-q-tensor/1.0.0
🌐 Website	photon-q.netlify.app
📊 Dashboard	photon-q.netlify.app/dashboard
📚 Docs	photon-q.netlify.app/docs
📑 Reports	photon-q.netlify.app/reports
🗄️ Zenodo	doi.org/10.5281/zenodo.19729926
👤 ORCID	orcid.org/0009-0003-8903-0029

---

📄 License

This project is licensed under the MIT License — see LICENSE for details.

Copyright © 2026 Samir Baladi · Ronin Institute / Rite of Renaissance

All optical validation data collected with institutional access agreements.
Quantum information benchmarks derived from open-science experimental records.

---

⟨ PHOTON-Q ⟩ — Making photonic decoherence visible, measurable, and correctable.

With a 94.7% mean QOEI and 8.7× coherence time extension, PHOTON-Q transforms
quantum-optical system management from reactive decoherence response to predictive
phase-coherent intelligence — at the speed of light.

---

🌐 Website · 📊 Dashboard · 📚 Docs · 🗄️ Zenodo · 🦊 GitLab

Version 1.0.0 · MIT License · DOI: 10.5281/zenodo.19729926 · ORCID: 0009-0003-8903-0029