qiskit-machine-learning and scikit-qulacs
These are competitors offering overlapping functionality—both provide quantum neural network frameworks for machine learning tasks—though qiskit-machine-learning has vastly greater adoption and maturity, making scikit-qulacs a niche alternative built on a different quantum computing backend (Qulacs instead of Qiskit).
About qiskit-machine-learning
qiskit-community/qiskit-machine-learning
An open-source library built on Qiskit for quantum machine learning tasks at scale on quantum hardware and classical simulators
This library helps quantum machine learning researchers and practitioners design and experiment with machine learning models that leverage quantum computing principles. It takes classical datasets as input and produces classification or regression models that can run on quantum hardware or simulators. Users are typically quantum algorithm developers or scientists exploring the cutting edge of quantum AI.
About scikit-qulacs
Qulacs-Osaka/scikit-qulacs
scikit-qulacs is a library for quantum neural network. This library is based on qulacs and named after scikit-learn.
This library helps quantum computing researchers and developers build and experiment with quantum neural networks. You provide your quantum data and model configurations, and it outputs a trained quantum neural network that can be used for various quantum machine learning tasks. It's designed for those working on the cutting edge of quantum algorithm development.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work