Topological Quantum Compiler (TQC)

The first universal compiler for quantum computers based on topological principles.

TQC moves beyond fragile gate-based quantum operations by compiling quantum algorithms into fault-tolerant braiding operations of anyonic quasiparticles. This revolutionary approach promises inherently stable quantum computation through topological protection.

🌟 Key Features

• Topological Compilation: Translate quantum circuits into anyonic braid operations
• Fault Tolerance: Inherent error protection through topological properties
• Multiple Anyon Types: Support for Fibonacci, Ising, and other anyonic systems
• Advanced Simulation: Efficient tensor network simulation of many-anyon systems
• Optimization: Solovay-Kitaev-style approximation algorithms for braid optimization
• Visualization: Generate beautiful braid diagrams and topological visualizations

🚀 Quick Start

from tqc import TopologicalCompiler, FibonacciAnyons
from qiskit import QuantumCircuit

# Create a simple quantum circuit
qc = QuantumCircuit(2, 2)
qc.h(0)
qc.cx(0, 1)
qc.measure_all()

# Compile to topological braids
compiler = TopologicalCompiler(anyon_type=FibonacciAnyons())
braid_program = compiler.compile(qc)

# Simulate the braided computation
result = braid_program.simulate(shots=1000)
print(f"Measurement results: {result.counts}")

# Visualize the braid
braid_program.visualize_braid(output="bell_state_braid.svg")

📦 Installation

# Via pip
pip install topological-quantum-compiler

# Via poetry
poetry add topological-quantum-compiler

# Development installation
git clone https://github.com/krish567366/TQC.git
cd TQC
poetry install

🔬 What Makes TQC Revolutionary

Traditional quantum computers suffer from:

• Fragile qubits sensitive to environmental noise
• High error rates requiring extensive error correction
• Limited coherence times constraining algorithm complexity

TQC solves these problems by encoding quantum information in the topological properties of anyonic braids, which are:

• Naturally fault-tolerant - protected by energy gaps
• Stable against local perturbations - only global changes affect computation
• Scalable - complexity grows polynomially with system size

📚 Documentation

• Installation Guide
• Quick Start Tutorial
• API Reference
• Advanced Examples

🎯 Example Applications

# VQE with topological compilation
from tqc.algorithms import TopologicalVQE
from tqc.chemistry import H2Molecule

molecule = H2Molecule(bond_length=0.74)
vqe = TopologicalVQE(molecule, anyon_type="fibonacci")
energy = vqe.run()

# Quantum machine learning with anyonic features
from tqc.ml import AnyonicFeatureMap

feature_map = AnyonicFeatureMap(n_qubits=4, anyon_type="ising")
quantum_kernel = feature_map.to_kernel()

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

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

• Created by Krishna Bajpai (krishna@krishnabajpai.me)
• Based on groundbreaking research in topological quantum computation
• Inspired by the work of Kitaev, Freedman, and other pioneers in the field
• Built on the shoulders of excellent libraries like Qiskit, JAX, and NumPy

📞 Support

• 📖 Documentation
• 🐛 Issue Tracker
• 💬 Discussions
• 📧 Contact: Krishna Bajpai krishna@krishnabajpai.me

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"The future of quantum computing is topological." - Krishna Bajpai, TQC Creator