aiqbench analysis
This paper introduces Sim-HVQC, a hybrid quantum-classical framework designed to enhance multi-class classification tasks by integrating adaptive feature weighting with variational quantum circuits. By addressing limitations in previous binary-only studies, the research offers a scalable approach that improves parameter efficiency and interpretability across standard image datasets. The work represents a significant step forward in leveraging near-term quantum hardware for practical machine learning applications without requiring full fault-tolerant systems.
Hybrid Variational Quantum-Classical Framework with Adaptive Weighting and Efficiency Assessment
arXiv:2609.28491v1 Announce Type: cross 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
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 this https URL SimAM-HVQC
Quantum Physics (quant-ph); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
arXiv:2609.28491 [quant-ph]
(or arXiv:2609.28491v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2609.28491 Focus to learn more arXiv-issued DOI via DataCite
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