Hybrid Variational Quantum-Classical Framework with Adaptive Weighting and Efficiency Assessment
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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BibTeX
@misc{rai2026hybrid,
title = {{Hybrid Variational Quantum-Classical Framework with Adaptive Weighting and Efficiency Assessment}},
author = {Dilli Hang Rai},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.28491},
eprint = {2609.28491},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.28491},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Hybrid Variational Quantum-Classical Framework with Adaptive Weighting and Efficiency Assessment
AU - Dilli Hang Rai
PY - 2026
DA - 2026/09/01
PB - arXiv
DO - 10.48550/arxiv.2609.28491
UR - https://arxiv.org/abs/2609.28491
AB - 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
ER -