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https://github.com/angrysky56/quantum-framework
Theoretical basis and prototype concept for an AI native OS as an infinite Turing Machine.
https://github.com/angrysky56/quantum-framework
ai architecture fractal infinite node os quantum turing-machine vector-database
Last synced: 2 days ago
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Theoretical basis and prototype concept for an AI native OS as an infinite Turing Machine.
- Host: GitHub
- URL: https://github.com/angrysky56/quantum-framework
- Owner: angrysky56
- Created: 2024-12-13T01:52:52.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2024-12-13T02:36:08.000Z (about 2 months ago)
- Last Synced: 2024-12-13T03:17:30.788Z (about 2 months ago)
- Topics: ai, architecture, fractal, infinite, node, os, quantum, turing-machine, vector-database
- Language: JavaScript
- Homepage:
- Size: 2.78 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Quantum Framework
The Quantum Framework is an innovative platform designed to harness the power of high-dimensional vector spaces, serving as a practical realization of an AI-driven infinite Turing machine. This framework enables the representation and manipulation of complex data structures, processes, and abstract concepts within a unified, scalable, and adaptable vector space.
## Key Features
- **High-Dimensional Vector Representation**: Encode diverse entities, relationships, and processes into high-dimensional vectors, facilitating seamless integration and processing across various data modalities.
- **Dynamic and Adaptive Computation**: Utilize AI-driven mechanisms to allow the framework to learn, adapt, and evolve over time, optimizing computations and enabling emergent behaviors.
- **Parallel Processing**: Leverage the inherent parallelism of vector operations to efficiently handle complex tasks and large datasets.
- **Scalability**: Employ a modular and hierarchical structure to manage the infinite-dimensional space, ensuring efficient computation and resource utilization.
## Theoretical Foundation
Inspired by the concept of a Turing machine with an infinite tape, the Quantum Framework extends this paradigm by incorporating:
- **Abstract Universality**: The framework's vector space can represent any computable function or logical structure, making it adaptable to a wide range of tasks and problems.
- **AI-Driven Intelligence**: By embedding AI at its core, the framework transcends traditional deterministic computation, enabling dynamic learning, optimization, and goal-oriented processing.
- **Emergent Behavior**: The system's ability to self-organize and adapt leads to the emergence of novel solutions and behaviors, reflecting a form of artificial general intelligence.
## Practical Applications
- **Data Integration**: Unify various data types—such as text, images, and audio—into a cohesive vector space for comprehensive analysis and processing.
- **Complex Task Management**: Represent and manage intricate processes and workflows within the vector space, facilitating efficient task scheduling and resource allocation.
- **Advanced Search and Retrieval**: Implement AI-driven search mechanisms that leverage learned relevance metrics to retrieve information based on contextual similarity.
## Getting Started
To explore the capabilities of the Quantum Framework, please refer to the [documentation](https://github.com/angrysky56/quantum-framework/wiki) and [Docker Quickstart](https://github.com/angrysky56/quantum-framework/blob/main/%23%20quantum_framework%20containerization.txt) for installation instructions, tutorials, and examples. Once you have the Docker containers running I suggest using VS Code to run the [notebook](https://github.com/angrysky56/quantum-framework/blob/Nexus-Prime/notebooks/quantum_pattern_debugging_fixed.ipynb) for the example in the image
## Contributing
We welcome contributions from the community. Please review our [contributing guidelines](CONTRIBUTING.md) to get started.
## License
This project is licensed under the [MIT License](LICENSE).
---
# Quantum Visualization Framework
An integrated system for visualizing and analyzing quantum mechanical systems with focus on orbital dynamics and state evolution.
## Mathematical Framework
### Core Equations
1. **Schrödinger Equation**:
```
iℏ ∂Ψ/∂t = HΨ
```
2. **Wavefunction**:
```
Ψ(r,θ,φ) = R_{nl}(r)Y_{lm}(θ,φ)
```
3. **Measurement**:
```
P(x) = |⟨x|Ψ⟩|²
```
### System Architecture
#### Computational Components
- **State Evolution**: Advanced numerical integration
- **Visualization Engine**: Real-time 3D rendering
- **Parameter Space**: Interactive quantum number exploration
#### Implementation Stack
```yaml
Framework:
Computation:
- NumPy/SciPy: Core numerics
- Numba: JIT compilation
- CuPy: GPU acceleration
Visualization:
- Plotly: Interactive 3D
- Dash: Web interface
- pythreejs: Advanced rendering
Testing:
- pytest: Unit testing
- hypothesis: Property testing
```
## Usage
1. Start the environment:
```bash
docker-compose up
```
2. Access interfaces:
- Jupyter Lab: http://localhost:8888
- Visualization: http://localhost:8050
## Development
### Testing
```bash
# Run all tests
docker-compose run quantum_engine pytest
# Run specific test
docker-compose run quantum_engine pytest tests/test_quantum_system.py
```
### Adding Features
1. Implement in `quantum_system.py`
2. Add tests in `tests/`
3. Update visualization in `visualization/app.py`
## Architecture
The system uses a modular architecture with:
1. **Core Engine**
- Quantum state computation
- Hamiltonian evolution
- Measurement operators
2. **Visualization Layer**
- Real-time state rendering
- Interactive parameters
- Probability density plots
3. **Testing Framework**
- Property-based verification
- Physical constraints
- Numerical stability
## Contributing
1. Fork repository
2. Create feature branch
3. Implement changes
4. Add tests
5. Submit pull request