Haul Quantum

A lightweight, extensible quantum computing framework for Python, designed for research and prototyping. Haul Quantum provides:

• Pure-Python simulation with statevector backends.
• Fluent API via Engine or direct QuantumCircuit.
• Chainable gates: H, X, CNOT, RX, RY, RZ, and more.
• Noise modeling and batch simulation support.
• Torch integration for hybrid quantum-classical neural nets.

🚀 Installation

pip install haul-quantum

Or install the latest development build:

git clone https://github.com/amirewontmiss/haul_quantum.git
cd haul_quantum
pip install -e .[dev]

🎯 Quick Start

Using the Engine

from haul_quantum.core.engine import Engine

# Create a 2-qubit circuit, build a Bell state:
eng = Engine(2)
out = eng.h(0).cnot(0,1).simulate()
print(out)  # [0.707+0j, 0.707+0j, 0+0j, 0+0j]

# Measure probabilities:
probs = eng.measure()
print(probs)  # {'00': 0.5, '01': 0.5}

Direct Circuit API

from haul_quantum.core.circuit import QuantumCircuit
from haul_quantum.core.gates import RX, H, CNOT

qc = QuantumCircuit(3)
qc.h(0).rx(1.23)(1).cnot(0,2)
state = qc.simulate()

📚 API Reference

Engine

QuantumCircuit

Same API as Engine, but stateless. Useful for circuit transformations, compilation, and exporting without an Engine wrapper.

🔌 Features

• Statevector simulator: Pure NumPy backend, no external dependencies.
• NoiseModel: Apply bit_flip, phase_flip, depolarizing channels.
• Batch simulation: Collect histograms over many shots.
• Torch integration: Wrap circuits as torch.nn.Module for hybrid training.

🖋️ Contributing

Pull requests and issues welcome! Please read CONTRIBUTING.md for guidelines.

📄 License

MIT © amirewontmiss