QNLib: Quantum Nanoelectronics Laboratory Library

QNLib is a Python package for working with qudit (d-dimensional quantum) systems. Built on top of Google's Cirq framework, it provides tools for quantum circuit generation, noise modeling, and randomized benchmarking.

Installation

git clone https://github.com/qnl/qnlib.git
cd qnlib
pip install -e .

Features

Qudit Gates

• Complete set of qudit Clifford gates
• Pauli operators for d-dimensional systems
• Support for arbitrary unitary decomposition into SU(2) gates

Noise Models

• Amplitude damping channel for qutrits
• Depolarizing noise
• Trit-flip channel
• Random compilation for noise tailoring

Benchmarking Tools

• Randomized benchmarking for single and multi-qudit systems
• Efficient Clifford circuit sampling using tableau simulation
• Built-in visualization and analysis tools

Quick Start

from qnlib.benchmarking import QuditBenchmarking
from cirq import Simulator

# Create a 2-qutrit benchmarking instance
rb = QuditBenchmarking(
    num_qudits=2,
    dimension=3,
    sampler=Simulator()
)

# Generate and run benchmark circuits
circuits = rb.generate_benchmark_circuits(
    depths=[5, 10, 15],
    num_circuits=10
)

# Run noisy simulation
results = rb.simulate_benchmarking(
    depths=[5, 10, 15],
    noise_levels=[0.01, 0.05]
)

Example Use Cases

Randomized Benchmarking

See examples/qudit_rb.ipynb for:

• Single qutrit benchmarking
• Multi-qudit systems
• Noise characterization

Circuit Compilation

See examples/sun_decomp.ipynb for:

• SU(N) decomposition into SU(2) gates
• Random circuit compilation
• Hardware-efficient gate sequences

Noise Studies

See examples/random_compiling.ipynb for:

• Noise tailoring techniques
• Random compilation methods
• Error mitigation strategies

Basic Qutrit Operations

See examples/qutrits_in_cirq.ipynb for:

• Basic qutrit gate operations
• Circuit construction
• Measurement and simulation

Core Components

Tableau Simulation

from qnlib.benchmarking import Tableau

# Generate random 2-qutrit Clifford circuit
tableau = Tableau(num_qudits=2, dimension=3)
tableau.populate_and_sweep()
circuit = tableau.circuit

Noise Channels

from qnlib.noise import AmplitudeDamping

# Create qutrit amplitude damping channel
noise = AmplitudeDamping(
    gamma_10=0.01,  # |1⟩ → |0⟩ probability
    gamma_20=0.01,  # |2⟩ → |0⟩ probability
    gamma_21=0.01   # |2⟩ → |1⟩ probability
)

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

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