Tetryonic Quasi-Tessellation Bit Sign (TQTBS)

A quantum-inspired computing system that combines multi-state bits with pattern-based operations for advanced computation and data representation.

Features

• Multi-State Representation

  • Classical states (0, 1)
  • Quantum-inspired states (Ψ, +, -)
  • Pattern-based operations
  • State transitions

• Grid-Based Storage

  • Dynamic grid sizing
  • Quasi-periodic patterns
  • Neighbor-aware operations
  • Sparse representation

• Quantum-Inspired Operations

  • Superposition
  • State collapse
  • Pattern transformation
  • Grid evolution

Installation

# Clone the repository
git clone https://github.com/yourusername/tqtbs.git
cd tqtbs

# Install dependencies
pip install -r requirements.txt

Quick Start

from tqtbs.core import TetryonicBit
from tqtbs.storage import QuasiTessellationGrid

# Create a TetryonicBit
bit = TetryonicBit("0")
bit.quantum_superposition()  # Enter superposition
bit.collapse_superposition()  # Collapse to classical state

# Create a grid
grid = QuasiTessellationGrid(3)
grid.initialize_grid()
grid.display_grid()

Benchmarks

The system includes comprehensive benchmarks comparing TQTBS with IEEE-754:

# Run focused benchmarks
python experiments/focused_benchmark.py

# Run general benchmarks
python experiments/benchmark_metrics.py

Key Performance Metrics

1. Quantum Operations

   • Superposition: 0.2-0.4 μs
   • State Collapse: 0.4-0.7 μs
   • Pattern Transform: 8-37 μs

2. Pattern Recognition

   • Small Patterns (3×3): ~15 μs
   • Medium Patterns (5×5): ~27 μs
   • Large Patterns (7×7): ~45 μs

Documentation

• Benchmark Comparison
• API Reference
• Pattern Operations
• Grid Management

Examples

Basic State Management

# Create and manipulate TetryonicBits
bit = TetryonicBit("0")
bit.quantum_superposition()
print(f"Current state: {bit.state}")  # Ψ
bit.collapse_superposition()
print(f"Collapsed state: {bit.state}")  # 0 or 1

Pattern Recognition

# Create a pattern grid
grid = QuasiTessellationGrid(3)
pattern = [
    [1, 1, 1],
    [0, 1, 0],
    [1, 1, 1]
]

# Initialize grid with pattern
for i in range(3):
    for j in range(3):
        if pattern[i][j]:
            grid.update_cell(i, j, "1")
        else:
            grid.update_cell(i, j, "0")

# Display the grid
grid.display_grid()

Quantum Operations

# Create a quantum-inspired pattern
grid = QuasiTessellationGrid(5)
for i in range(5):
    for j in range(5):
        bit = TetryonicBit("1")
        bit.quantum_superposition()
        grid.update_cell(i, j, bit.state)

# Display quantum pattern
grid.display_grid()

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

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

Acknowledgments

• IEEE-754 floating-point standard
• Quantum computing principles
• Pattern recognition algorithms
• Grid-based computation methods

Contact

• Project Link: https://github.com/yourusername/tqtbs
• Documentation: https://tqtbs.readthedocs.io