International Conference on Artificial Intelligence for Medicine, Health and Care (AIxMHC)

Enhancing the Performance and Speed of Quantum Support Vector Classifier via Neural Quantum Embedding and Tensor Network

Aninda Astuti, Tai-Yue Li, Simon Chong Wee See, Ka‐Lok Ng

Published Oct 13, 2025• 1 citation

Abstract

Quantum Machine Learning (QML), which combines quantum computing and machine learning, holds significant promise for solving complex problems. This study uses QML algorithm to find gene expression biomarkers related to metastasis in clear cell renal cell carcinoma (ccRCC), the most common type of kidney cancer. Traditional ML methods often struggle to find reliable biomarkers for metastasis. To address these challenges, we proposed a two-stage classification framework inspired by Neural Quantum Embedding (NQE), aiming to enhance both the execution time and classification accuracy of Quantum Support Vector Classifier (QSVC) relative to classical SVC. We evaluate the effectiveness of combining NQE with QSVC and compare it to the performance of NQE combined with classical SVC across various biomedical datasets. To further improve computational efficiency, we integrate Tensor Network (TN) techniques into the QSVC implementation. Our results show that, using 12 principal components (PCs), the F1 score of the NQE+TN+QSVC model improves by a factor of 2.02 compared to NQE+QSVC and by 2.15 compared to NQE+SVC. Furthermore, the integration of TN reduces the QSVC training time by up to 21.82-fold when using 12 principal components. We have developed an effective approach that improve the classification performance and reduce the simulation time.

DOI: 10.1109/aixmhc65380.2025.00018

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