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Develops a hybrid quantum-classical machine learning pipeline for classification tasks, indicating high accuracy and scalability.
An approach to the development of hybrid quantum-classical data analysis pipelines based on the composition of ready-made quantum templates of the PennyLane framework is proposed. A hybrid quantum-classical machine learning pipeline for classification tasks has been developed and successfully tested, demonstrating high accuracy on a number of test datasets and the ability to scale the architecture.
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Zrelov et al. (2026) studied this question.
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