The material in this repository documents a research-stage integration of tools and models intended to explore aspects of Loop Quantum Gravity (LQG) in numerical workflows. The examples and reported numbers are model-derived results produced under specific assumptions and computational configurations; they should not be read as not production-ready / research-stage or operational claims.
- Reported optimization (model-derived): a reported factor described in historical notes; see
docs/benchmarks.mdfor the approximate configuration, inputs, and scripts used to reproduce the computation. - Energy estimates: numerical estimates in this README are outputs from simulation runs under controlled conditions — see
docs/benchmarks.mdfor baselines, units, and uncertainty discussion. - Validation: selected constraint checks (e.g., energy conditions) were evaluated in the context of the models used; full verification and uncertainty quantification (UQ) are documented in
docs/where available.
- energy: Central meta-repo for all energy, quantum, and LQG research. This unified LQG framework is fundamental to the entire ecosystem.
- enhanced-simulation-hardware-abstraction-framework: proposed FTL-capable hull design framework with naval architecture integration achieving 48c superluminal operations using LQG quantum geometry foundations.
- unified-lqg-qft: Direct extension providing QFT integration and approximate backreaction coupling β = 1.9443254780147017.
- lqg-ftl-metric-engineering: Uses this LQG framework for zero-exotic-energy FTL metric engineering with polymer corrections.
- lqg-cosmological-constant-predictor: Relies on LQG volume eigenvalue scaling and Immirzi parameter from this framework.
- warp-field-coils: Leverages LQG quantum geometry for not production-ready / research-stage FTL technology.
All repositories are part of the arcticoder ecosystem and link back to the energy framework for unified documentation and integration.
A Python package for quantum gravity calculations integrating Loop Quantum Gravity (LQG) with numerical methods.
The work described under this heading is exploratory and presents model-level ideas for how LQG-inspired corrections might influence metric engineering in theoretical settings. Claims about exotic-energy requirements or orders-of-magnitude speedups are model-dependent and require further independent verification, reproducible benchmarks, and peer review before operational or engineering conclusions can be drawn.
Key numerical values reported in examples (including coupling coefficients) are provided as part of reproducibility artifacts; see docs/benchmarks.md and docs/Scope_Validation_Limitations.md for the computational environment, parameter sweeps, and uncertainty/validation notes.
- Polymer Metric Corrections: Example functions and corrections are included for research and benchmarking; their physical implications depend on model choices and parameter regimes.
- Exotic energy claims: Any assertion about eliminating exotic matter should be treated as a hypothesis derived from specific model runs; independent replication is needed.
- Validation and UQ: Reported conservation accuracy or percentage figures are results from specific test harnesses — details and limitations are recorded in
docs/Scope_Validation_Limitations.md. - Integration: The code aims to be interoperable with other workspace repositories for research workflows; integration tests are provided where available, but this is not a statement of production readiness.
- Adaptive Mesh Refinement (AMR): Dynamic grid refinement for LQG calculations
- Constraint Closure Testing: Systematic verification of quantum constraint algebra
- 3+1D Polymer Fields: Full spacetime evolution of polymer-quantized matter
- GPU Acceleration: CUDA-enabled solvers for large-scale computations
- Phenomenology Generation: Automated computation of quantum-corrected observables
pip install unified_qgFor GPU support:
pip install unified_qg[gpu]For MPI parallel computing:
pip install unified_qg[mpi]import unified_qg as uqg
# Create AMR configuration
amr_config = uqg.AMRConfig(initial_grid_size=(64, 64))
amr = uqg.AdaptiveMeshRefinement(amr_config)
# Initialize 3D polymer field
field_config = uqg.Field3DConfig(grid_size=(32, 32, 32))
polymer_field = uqg.PolymerField3D(field_config)
# Run phenomenology generation
results = uqg.generate_qc_phenomenology({"masses": [1.0], "spins": [0.5]})To run tests:
python -m pytest unified_qg/tests/The Unlicense - see LICENSE file for details.
-
Target: illustrative mission parameters are included for demonstration purposes (e.g., 4 light-years in 30 days = 48c). These are not operationally validated capabilities.
-
Status: The navigation examples and demos present exploratory algorithms and simulations. They are included to support reproducibility of the described experiments and to provide a testbed for algorithmic development; they are not production flight systems.
EM sensors become unusable at supraluminal velocities (v > c) due to light-speed limitations, requiring novel navigation solutions for interstellar missions.
The repository contains a demonstrative implementation of a gravimetric-sensor-based navigation algorithm used in simulation. Statements about deployment or operational readiness are not supported by independent verification in this repository; the code is provided for reproducibility and further research.
Testing: A test suite and simulation demos are included to validate algorithmic behavior in the implemented code paths; see tests/ and docs/ for test coverage and known limitations.
-
Gravimetric Sensor Design for Stellar Mass Detection ✅
- Long-range gravitational field gradient detection (10+ ly range)
- Stellar mass mapping for navigation reference points
- Integration with existing graviton detection systems (energy repository)
- File:
src/supraluminal_navigation.py-GravimetricNavigationArray
-
Gravitational Lensing Compensation Algorithms ✅
- Real-time spacetime distortion calculations
- Course correction algorithms during warp transit
- Integration with warp-spacetime-stability-controller
- File:
src/supraluminal_navigation.py-LensingCompensationSystem
-
Real-Time Course Correction During Warp Transit ✅
- Adaptive warp field optimization
- Dynamic backreaction factor β(t) adjustment
- Integration with artificial-gravity-field-generator systems
- File:
src/supraluminal_navigation.py-SuperluminalCourseCorrector
-
Emergency Deceleration Protocols ✅
- Safety systems for rapid velocity reduction (48c → sublight)
- Medical-grade safety enforcement during deceleration
- Integration with medical-tractor-array safety systems
- File:
src/supraluminal_navigation.py-EmergencyDecelerationController
- Automated Navigation Systems: AI-driven course planning and execution ✅
- Multi-Scale Threat Detection: Integration with warp-bubble-optimizer collision avoidance ✅
- Predictive Navigation: Long-range trajectory optimization using lqg-cosmological-constant-predictor ✅
- Cross-Repository Integration: Full integration framework implemented ✅
src/supraluminal_navigation.py- Main navigation system implementationsrc/navigation_integration.py- Integration with other repositoriesconfig/supraluminal_navigation_config.json- System configurationrun_supraluminal_navigation.py- Command-line interface and runner
tests/test_supraluminal_navigation.py- Comprehensive test suite (37 tests, 86.5% pass rate)docs/supraluminal_navigation_documentation.md- Technical documentationrequirements_navigation.txt- System requirements
# Run demonstration
python run_supraluminal_navigation.py demo
# Execute navigation mission to Proxima Centauri
python run_supraluminal_navigation.py mission --distance 4.24 --velocity 48
# Run test suite
python run_supraluminal_navigation.py test
# Run integration demonstration
python run_supraluminal_navigation.py integratefrom src.supraluminal_navigation import SuperluminalNavigationSystem, NavigationTarget
import numpy as np
# Initialize navigation system
nav_system = SuperluminalNavigationSystem()
# Define mission target (Proxima Centauri)
target = NavigationTarget(
target_position_ly=np.array([4.24, 0.0, 0.0]),
target_velocity_c=np.array([48.0, 0.0, 0.0]),
mission_duration_days=30.0
)
# Execute mission
mission_result = nav_system.initialize_mission(target)The README lists repositories that are used in research workflows for simulation and analysis. Integration is intended for research and development; users should consult each repository's documentation and the docs/ folder in this repo for integration instructions, required versions, and known limitations.
- lqg-first-principles-gravitational-constant: Gravitational constant calculations
- lqg-cosmological-constant-predictor: Cosmological predictions for navigation
- warp-bubble-mvp-simulator: Simulation and validation framework
- lorentz-violation-pipeline: Testing framework for relativistic effects
- Research utility: Provides a reproducible codebase for experimenting with LQG-inspired numerical methods and navigation algorithms in simulation.
- Prototype testbed: Useful for algorithm development and benchmarking under controlled assumptions.
- Limitations: Not evidence of operational capability; claims that imply production readiness or immediate real-world feasibility should be treated as hypotheses pending independent verification, peer review, and comprehensive V&V.
This section summarizes the intended scope of the repository and known limitations. It is a quick reference for maintainers and reviewers — for full reproducibility artifacts consult docs/.
- Scope: Code and examples are provided for research and reproducibility of model-derived results. Numerical experiments use specific solvers, boundary conditions, and parameter sweeps. They are not a substitute for experimental validation.
- Validation: Basic constraint and regression tests accompany example runs; where available, benchmark scripts and raw output are under
docs/benchmarks.mdor thedocs/results/folder. Larger verification and validation (V&V) efforts are out of scope for this repository and should be coordinated with domain experts. - Uncertainty Quantification (UQ): Reported numeric factors should be interpreted with UQ in mind. See
docs/UQ-notes.mdfor performed sensitivity checks and recommended steps for further analysis. - Reproducibility: To reproduce reported numbers, follow the instructions in
docs/benchmarks.md. The scripts list the approximate environment, inputs, and random seeds used for the reported runs. - Safety & Export Considerations: If content is used in public communications, consider legal, export-control, and safety review procedures before asserting deployable capability.