This repository contains a cutting-edge library for building quantum machine learning models, with support for popular quantum computing frameworks like Qiskit and Cirq. The library includes a range of pre-built quantum machine learning algorithms, as well as tools for building and training custom models. With its modular design and easy-to-use API, this library makes it simple to get started with quantum machine learning, even for users with little or no prior experience in quantum computing. Whether you're looking to explore the potential of quantum computing for machine learning, or you're a seasoned quantum computing expert looking for a powerful and flexible toolkit, this library has something to offer.
This repository contains a cutting-edge library for building quantum machine learning models, with support for popular quantum computing frameworks like Qiskit and Cirq.
Pre-built quantum machine learning algorithms, including quantum support vector machines, quantum neural networks, and quantum principal component analysis Tools for building and training custom quantum machine learning models Modular design and easy-to-use API for rapid prototyping and experimentation Support for popular quantum computing frameworks like Qiskit and Cirq Comprehensive documentation and examples to help you get started quickly
To install the Quantum Machine Learning Library, simply run the following command:
pip install quantum_ml_library
Alternatively, you can clone the repository and install it manually:
git clone https://github.com/KOSASIH/quantum_ml_library.git
cd quantum_ml_library
pip install .
To use the Quantum Machine Learning Library, simply import the desired modules and start building your models. Here's an example of how to use the library to build a quantum support vector machine:
from quantum_ml_library.algorithms import QSVM
from quantum_ml_library.datasets import load_iris
# Load the iris dataset
X, y = load_iris()
# Create a quantum support vector machine
qsvc = QSVM()
# Train the model
qsvc.fit(X, y)
# Make predictions
predictions = qsvc.predict(X)
For more information on how to use the Quantum Machine Learning Library, please refer to the documentation.
We welcome contributions to the Quantum Machine Learning Library! If you'd like to contribute, please fork the repository and submit a pull request.
The Quantum Machine Learning Library is licensed under the MIT License.
The Quantum Machine Learning Library was developed with support from the Quantum Information Science and Engineering Network.