Adaptive Quantum Neural Networks: Tensor Representation Learning

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum Neural Networks (QNNs) hold tremendous potential for revolutionizing machine learning, particularly in complex data analysis and pattern recognition. However, training QNNs can be computationally expensive and require significant expertise in network architecture and optimization. This paper proposes a novel approach to QNN training – Adaptive Tensor Representation Learning (ATRL) – which dynamically adjusts the tensor representation of the network based on the input data's unique characteristics. We demonstrate that ATRL significantly improves model performance across a range of datasets compared to traditional training methods. The method leverages the inherent properties of quantum neural networks to automatically learn and refine the tensor structure, fostering a more robust and adaptable model. This research addresses a critical bottleneck in QNN development, offering a pathway towards more efficient and effective training.

DOI: 10.5281/zenodo.22299018

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