lealcalejandro/lazyqml repository overview

⁠LazyQML

LazyQML is a Python library designed to streamline, automate, and accelerate experimentation with Quantum Machine Learning (QML) architectures, right on classical computers.

With LazyQML, you can:

• 🛠️ Build, test, and benchmark QML models with minimal effort.

• ⚡ Compare different QML architectures, hyperparameters seamlessly.

• 🧠 Gather knowledge about the most suitable architecture for your problem.

⁠✨ Why LazyQML?

• Rapid Prototyping: Experiment with different QML models using just a few lines of code.

• Automated Benchmarking: Evaluate performance and trade-offs across architectures effortlessly.

• Flexible & Modular: From basic quantum circuits to hybrid quantum-classical models—LazyQML has you covered.

⁠Documentation

For detailed usage instructions, API reference, and code examples, please refer to the official LazyQML documentation⁠.

⁠Requirements

• Python >= 3.10

⁠Installation

To install lazyqml, run this command in your terminal:

pip install lazyqml

This is the preferred method to install lazyqml, as it will always install the most recent stable release.

If you don't have pip⁠ installed, this Python installation guide⁠ can guide you through the process.

⁠From sources

To install lazyqml from sources, run this command in your terminal:

pip install git+https://github.com/QHPC-SP-Research-Lab/LazyQML

⁠Example

from sklearn.datasets import load_iris
from lazyqml import *

# Load data
data = load_iris()
X = data.data
y = data.target

classifier = QuantumClassifier(nqubits={4}, classifiers={Model.QNN, Model.QSVM}, epochs=10)

# Fit and predict
classifier.fit(X=X, y=y, test_size=0.4)

⁠Quantum and High Performance Computing (QHPC) - University of Oviedo

• José Ranilla Pastor - [email protected]⁠
• Elías Fernández Combarro - [email protected]⁠
• Diego García Vega - [email protected]⁠
• Fernando Álvaro Plou Llorente - [email protected]⁠
• Alejandro Leal Castaño - [email protected]⁠
• Group - https://qhpc.uniovi.es⁠

⁠Citing

If you used LazyQML in your work, please cite:

• García-Vega, D., Plou Llorente, F., Leal Castaño, A., Combarro, E.F., Ranilla, J.: Lazyqml: A python library to benchmark quantum machine learning models. In: 30th European Conference on Parallel and Distributed Processing (2024)

⁠License

• Free software: MIT License