analiza aiqbench
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
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Text integral sub licența deschisă a publicației · 213 cuvinte
Subiecte conexe
Hybrid Quantum-Classical Neural Network Tackles Multi-Class Datasets
Explore Quantum · 25 sept. 2026
Feature-Adaptive Fusion in Hybrid Quantum-Classical Neural Networks for Robust Biomedical Image Classification
arXiv · 15 sept. 2026
Q-SpiRL: Quantum Spiking Reinforcement Learning for Adaptive Robot Navigation
arXiv · 24 iul. 2026
Adaptive Quantum Physics-Informed Neural Networks for Differential Equations with Applications to Fluid Dynamics
arXiv · 4 aug. 2026