metatron-qso 0.1.0

Python SDK for Metatron Quantum State Operator - High-performance quantum computing on sacred geometry

Metatron QSO Python SDK

Python bindings for the Metatron Quantum State Operator - a high-performance quantum computing framework built on sacred geometry.

Overview

The metatron_qso Python package provides an easy-to-use interface to the Rust-based quantum computing core, enabling:

• Quantum Walks: Continuous-time quantum walks on the Metatron Cube
• QAOA: Quantum Approximate Optimization Algorithm for combinatorial problems
• VQE: Variational Quantum Eigensolver for ground state energy
• High Performance: Leveraging Rust's speed with Python's ease of use

Installation

From Source (Development)

Requirements:

• Python 3.8+
• Rust toolchain (install from rustup.rs)
• maturin (pip install maturin)

# Clone the repository
cd metatron_qso_py

# Install in development mode
maturin develop --release

# Or build a wheel
maturin build --release

Using pip (Future)

pip install metatron-qso

Quick Start

import metatron_qso

# Create the Metatron Cube graph
graph = metatron_qso.MetatronGraph()
print(graph)  # MetatronGraph(nodes=13, edges=78)

# Run a quantum walk
result = metatron_qso.run_quantum_walk(
    graph=graph,
    source_nodes=[0],  # Start at central node
    t_max=5.0,
    dt=0.1
)

# Access results
print(f"Final probabilities: {result['final_state']}")

API Reference

MetatronGraph

The core graph structure representing the Metatron Cube.

# Create the default Metatron graph
graph = metatron_qso.MetatronGraph()

# Get graph properties
num_nodes = graph.num_nodes()      # Returns: 13
num_edges = graph.num_edges()      # Returns: 78
adj_list = graph.adjacency_list()  # Returns: list of lists

run_quantum_walk

Execute a continuous-time quantum walk.

result = metatron_qso.run_quantum_walk(
    graph,              # MetatronGraph instance
    source_nodes,       # List of initial nodes (e.g., [0] or [1, 2, 3])
    t_max=10.0,        # Maximum evolution time
    dt=0.1             # Time step
)

# Returns dictionary with:
# - 'times': List of time points
# - 'probabilities': List of probability distributions
# - 'final_state': Final probability distribution

solve_maxcut_qaoa

Solve MaxCut optimization using QAOA.

result = metatron_qso.solve_maxcut_qaoa(
    graph,              # MetatronGraph instance
    depth=3,           # QAOA circuit depth (p)
    max_iters=100      # Maximum optimization iterations
)

# Returns dictionary with:
# - 'cut_value': Best cut value found
# - 'assignment': Binary node assignment (list of 0s and 1s)
# - 'approximation_ratio': Solution quality (0 to 1)
# - 'meta': Additional optimization metadata

run_vqe

Find ground state energy using VQE.

result = metatron_qso.run_vqe(
    graph,                              # MetatronGraph instance
    depth=2,                           # Ansatz depth
    max_iters=100,                     # Maximum iterations
    ansatz_type="hardware_efficient"   # Ansatz: "hardware_efficient",
                                       #         "metatron", or "efficient_su2"
)

# Returns dictionary with:
# - 'ground_state_energy': Computed ground state energy
# - 'classical_ground_energy': Exact energy for comparison
# - 'error': Absolute error
# - 'iterations': Number of optimization steps
# - 'final_state': Final quantum state probabilities

Examples

The examples/ directory contains complete demonstrations:

• 01_quantum_walk_basic.py: Basic quantum walk demo
• 02_qaoa_maxcut_basic.py: MaxCut optimization with QAOA
• 03_vqe_ground_state.py: Ground state energy computation

Run an example:

cd metatron_qso_py
python examples/01_quantum_walk_basic.py

Jupyter Notebooks

Interactive tutorials are available in notebooks/:

• QuantumWalk_Intro.ipynb: Comprehensive quantum walk tutorial with visualizations

To use notebooks:

pip install jupyter matplotlib numpy
cd metatron_qso_py/notebooks
jupyter notebook QuantumWalk_Intro.ipynb

Performance Notes

• Rust Backend: All computationally intensive operations run in optimized Rust code
• Parallelization: Many operations use rayon for multi-threaded execution
• Memory Efficiency: Minimal data copying between Python and Rust

Typical performance (Intel i7-12700K):

• Quantum Walk (single step): ~31 μs
• QAOA iteration (depth=3): ~2-5 ms
• VQE iteration (depth=2): ~3-8 ms

Development

Running Tests

# Rust tests
cd metatron_qso_py
cargo test

# Python tests (if implemented)
pytest tests/

Building Documentation

# Rust documentation
cargo doc --open

# Python documentation
pdoc metatron_qso

Architecture

The Python SDK architecture:

┌─────────────────────────────────────┐
│   Python API (metatron_qso)        │
│   - MetatronGraph class             │
│   - run_quantum_walk()              │
│   - solve_maxcut_qaoa()             │
│   - run_vqe()                       │
└─────────────────────────────────────┘
                ↓ PyO3
┌─────────────────────────────────────┐
│   Rust Core (metatron-qso-rs)      │
│   - Graph structures                │
│   - Quantum state evolution         │
│   - VQA algorithms                  │
│   - Linear algebra (nalgebra)       │
└─────────────────────────────────────┘

Related Documentation

• Rust Core Guide
• Python SDK Guide
• VQA Implementation
• Root README

License

MIT License - see LICENSE for details.

Support

• Issues: GitHub Issues
• Documentation: Project Docs

Citation

If you use Metatron QSO in your research, please cite:

@software{metatron_qso,
  title = {Metatron Quantum State Operator},
  author = {Sebastian Klemm},
  year = {2024},
  url = {https://github.com/LashSesh/qso}
}