squlearn and scikit-qulacs
Both libraries provide scikit-learn-compatible interfaces to underlying quantum computing frameworks (sQUlearn wraps general quantum algorithms while scikit-qulacs specifically wraps the Qulacs simulator), making them ecosystem siblings that serve similar architectural purposes across different quantum backends rather than direct competitors.
About squlearn
sQUlearn/squlearn
scikit-learn interface for quantum algorithms
This project helps quantum machine learning researchers and practitioners experiment with and prototype quantum algorithms. It takes various quantum data encoding strategies and machine learning models as input, and outputs trained quantum models ready for evaluation. This is for users exploring quantum kernel methods and quantum neural networks for practical machine learning applications.
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.
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