Core Claim: Design a quantum machine learning algorithm based on adaptive tensor aggregation, accelerating quantum machine learning algorithms and enhancing algorithm generalization.

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum machine learning is a rapidly developing field with the potential to revolutionize various computational tasks. Traditional machine learning algorithms often rely on classical computational resources, limiting their scalability and performance. Adaptive tensor aggregation, a novel technique, offers a promising approach to address these limitations by dynamically adjusting the number of qubits and the state of each qubit, thereby optimizing quantum computations. This paper presents a design for a quantum machine learning algorithm, leveraging adaptive tensor aggregation, to accelerate quantum machine learning algorithms and improve generalization. We explore the core mechanisms and potential benefits of this approach, demonstrating its effectiveness through theoretical analysis and preliminary experimental results. The research addresses a key challenge in quantum machine learning: efficient utilization of quantum resources through dynamic qubit control. This work lays the groundwork for a new generation of quantum algorithms with enhanced generalization capabilities.

DOI: 10.5281/zenodo.22263776

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