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

arXiv: https://arxiv.org/abs/2609.28491 · HTML: https://mlsanity.com/paper/2609.28491/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 15 references that are in the map.
- First posted on arXiv 30 Aug 2026.

## Builds on (most cited first, 8 of 8)

- [Quantum machine learning for image classification](https://mlsanity.com/paper/2304.09224/) (2023)
- [An end-to-end trainable hybrid classical-quantum classifier](https://mlsanity.com/paper/2102.02416/) (2021)
- [Hybrid quantum-classical classifier based on tensor network and variational quantum circuit](https://mlsanity.com/paper/2011.14651/) (2020)
- [Multiclass classification using quantum convolutional neural networks with hybrid quantum-classical learning](https://mlsanity.com/paper/2203.15368/) (2022)
- [Variational Quantum Neural Networks (VQNNS) in Image Classification](https://mlsanity.com/paper/2303.05860/) (2023)
- [Continuous Variable Quantum MNIST Classifiers](https://mlsanity.com/paper/2204.01194/) (2022)
- [Hybrid Quantum-Classical Learning for Multiclass Image Classification](https://mlsanity.com/paper/2508.18161/) (2025)
- [Hybrid Quantum Neural Networks: Theory, Implementations, and Applications](https://mlsanity.com/paper/2608.01194/) (2026)

## Abstract (arXiv)

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