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arXiv · 2609.28491

Hybrid Variational Quantum-Classical Framework with Adaptive Weighting and Efficiency Assessment

Dilli Hang Rai
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Abstract

Hybrid quantum-classical neural networks have emerged as a promising approach for leveraging quantum computing in machine learning while mitigating current hardware limitations. This paper presents Sim-HVQC, a hybrid Deep Quantum Neural Network that couples an adaptive, parameter-free SimAM weighting module with classical feature extraction to preserve class-discriminative information prior to encoding into a Variational Quantum Circuit (VQC). Previous studies are restricted to binary classification [1] [2] [3] [4] [5]. In contrast, the proposed framework is trained and evaluated on various multi-class datasets(MNIST, KMNIST, Fashion-MNIST, and EMNIST). The framework further demonstrates reproducibility, parameter efficiency, and interpretability through multi-seed evaluation, parameter analysis, and latent/quantum feature inspection. The source code is publicly available at https://github.com/Dilli822/ SimAM-HVQC

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Dilli Hang Rai. 2026-08-30. Hybrid Variational Quantum-Classical Framework with Adaptive Weighting and Efficiency Assessment. https://arxiv.org/abs/2609.28491

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