Hybrid Variational Quantum-Classical Framework with Adaptive Weighting and Efficiency Assessment
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 ...
| Authors | Dilli Hang Rai |
|---|---|
| Primary category | quant-ph |
| Published | 30 August 2026 |
| Last updated at source | 30 August 2026 |
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