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Found 4 papers, 1 papers with code
sQUlearn -- A Python Library for Quantum Machine Learning
sQUlearn introduces a user-friendly, NISQ-ready Python library for quantum machine learning (QML), designed for seamless integration with classical machine learning tools like scikit-learn.
Reduction of finite sampling noise in quantum neural networks
We reduce this noise by introducing the variance regularization, a technique for reducing the variance of the expectation value during the quantum model training.
Quantum Gaussian Process Regression for Bayesian Optimization
We also show that quantum Gaussian processes can be used as a surrogate model for Bayesian optimization, a task that critically relies on the variance of the surrogate model.
A Nested Genetic Algorithm for Explaining Classification Data Sets with Decision Rules
Our goal in this paper is to automatically extract a set of decision rules (rule set) that best explains a classification data set.
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