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arXiv:2609.28491

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

Dilli Hang Rai

quant-phcs.AIcs.LGSubmitted Aug 30, 2026• Announced Sep 1, 2026• 0 citations
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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

AI Summary

The paper examines whether parameter-free attention before classical feature compression improves hybrid variational quantum–classical image classification across multiple multiclass datasets. Its SimAM-HVQC architecture applies the existing SimAM weighting module to images, then uses a fully connected encoder $784\rightarrow256\rightarrow128\rightarrow8$. The eight latent features are mapped to rotation angles using $\theta=\pi\tanh(F)$ and processed by an eight-qubit variational circuit with six strongly entangling layers and 144 trainable quantum parameters. Pauli-$Z$ expectations feed a classical classifier. Experiments use PennyLane’s state-vector simulator on a CPU, with 25 training epochs, a 70:15:15 train–validation–test split, and five random seeds. Reported classification accuracies are $97.57\pm0.05\%$ on MNIST, $87.98\pm0.08\%$ on Fashion-MNIST, $88.20\pm0.61\%$ on KMNIST, and $80.16\pm0.17\%$ on 47-class EMNIST Balanced. Removing SimAM gives respective accuracies of 97.48%, 87.51%, 88.23%, and 78.99%. Additional analyses vary training-data availability and inspect representations using PCA, saliency, occlusion sensitivity, and quantum-measurement responses. A CPU inference benchmark reports 236,258 total parameters and 0.8124 milliseconds per sample over 2,000 samples. The evaluation is restricted to simulated circuits and standard grayscale image benchmarks. The reported ablation isolates attention rather than the contribution of the quantum circuit; no matched fully classical baseline or hardware/noise evaluation is presented. The paper does not provide an explicit limitations discussion.

Some signs of AI-generated textOverclaimsIncrementalMissing comparisonPossible error

Error found: The abstract's blanket characterization of previous studies as binary-only conflicts with the paper's own acknowledgment and tabulation of prior multiclass work. The EMNIST data-utilization table reports 83.66–87.55% accuracy, versus 80.16 ± 0.17% in the principal results, without explaining the discrepancy. The reported parameter counts do not establish comparative parameter efficiency because the paper supplies no matched-performance baseline. The conclusion that the quantum component introduces limited computational overhead is unsupported by total simulator latency and parameter counts without an appropriate timing baseline.

Written by gpt-6.1-sol from the paper's full text. Automated: it can be wrong.

Comments
5 pages, 5 figures, 5 tables

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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
The paper examines whether parameter-free attention before classical feature compression improves hybrid variational quantum–classical image classification across multiple multiclass datasets. Its SimAM-HVQC architecture applies the existing SimAM weighting module to images, then uses a fully connected encoder 784→256→128→8784\rightarrow256\rightarrow128\rightarrow8784→256→128→8. The eight latent features are mapped to rotation angles using θ=πtanh⁡(F)\theta=\pi\tanh(F)θ=πtanh(F) and processed by an eight-qubit variational circuit with six strongly entangling layers and 144 trainable quantum parameters. Pauli-ZZZ expectations feed a classical classifier.
Experiments use PennyLane’s state-vector simulator on a CPU, with 25 training epochs, a 70:15:15 train–validation–test split, and five random seeds. Reported classification accuracies are 97.57±0.05%97.57\pm0.05\%97.57±0.05% on MNIST, 87.98±0.08%87.98\pm0.08\%87.98±0.08% on Fashion-MNIST, 88.20±0.61%88.20\pm0.61\%88.20±0.61% on KMNIST, and 80.16±0.17%80.16\pm0.17\%80.16±0.17% on 47-class EMNIST Balanced. Removing SimAM gives respective accuracies of 97.48%, 87.51%, 88.23%, and 78.99%. Additional analyses vary training-data availability and inspect representations using PCA, saliency, occlusion sensitivity, and quantum-measurement responses. A CPU inference benchmark reports 236,258 total parameters and 0.8124 milliseconds per sample over 2,000 samples.
The evaluation is restricted to simulated circuits and standard grayscale image benchmarks. The reported ablation isolates attention rather than the contribution of the quantum circuit; no matched fully classical baseline or hardware/noise evaluation is presented. The paper does not provide an explicit limitations discussion.
One key step in performing quantum machine learning (QML) on noisy intermediate-scale quantum (NISQ) devices is the dimension reduction of the input data prior to their encoding. Traditional principle component analysis (PCA) and neural networks have been used to perform this task; however, the classical and quantum layers are usually trained separately. A framework that allows for a better integration of the two key components is thus highly desirable. Here we introduce a hybrid model combining the quantum-inspired tensor networks (TN) and the variational quantum circuits (VQC) to perform supervised learning tasks, which allows for an end-to-end training. We show that a matrix product state based TN with low bond dimensions performs better than PCA as a feature extractor to compress data for the input of VQCs in the binary classification of MNIST dataset. The architecture is highly adaptable and can easily incorporate extra quantum resource when available.
Medical image classification is a critical component of modern healthcare; however, accurate diagnosis remains challenging due to limited annotated datasets, class imbalance, and the high dimensionality of medical imaging data. To address these challenges, a hybrid quantum-classical neural network (HQCNN) is proposed, integrating classical deep learning with variational quantum learning for medical image classification. The proposed architecture combines a five-layer convolutional neural network (CNN) for hierarchical feature extraction with a lightweight 4-qubit variational quantum circuit (VQC) incorporating quantum state encoding, superposition and entanglement mechanisms, and a quantum attention-Fourier (QAF) module. This hybrid design aims to improve nonlinear feature representation and quantum parameter efficiency while maintaining a shallow quantum circuit suitable for noisy intermediate-scale quantum (NISQ)-era constraints. Experimental evaluation on six MedMNIST benchmark datasets demonstrated competitive performance across both binary and multi-class classification tasks. HQCNN achieved 98.88% accuracy on the binary subset of PathMNIST (classes 0 vs. 1), 97.61% accuracy on the multi-class OrganAMNIST dataset, and 86.29% accuracy on BreastMNIST. Comparative experiments and statistical analyses demonstrated consistent improvements over the controlled BHQNN baseline, while component-wise ablation studies showed that the QAF module, superposition and entanglement mechanisms, and expressive parameterized rotations contributed to classification performance in a complementary and dataset-dependent manner. Moreover, HQCNN reduced the number of trainable quantum parameters by approximately 55.6% compared with the baseline hybrid quantum neural network (BHQNN). Noise-aware simulations further showed that the model retained relatively stable predictive performance under moderate depolarizing noise, supporting further evaluation under near-term quantum computing conditions. Overall, the results demonstrate that HQCNN provides a parameter-efficient hybrid quantum-classical framework for medical image classification and offers a promising foundation for further investigation of quantum-enhanced medical image analysis.
Abstract High-dimensional classification remains challenging for hybrid quantum–classical learning because conventional variational quantum feature learning methods often assign equal importance to all input features and do not explicitly account for feature uncertainty. This paper presents Uncertainty-Aware Variational Quantum Feature Learning (UVQFL) , a hybrid framework that combines adaptive feature weighting, uncertainty estimation, and variational quantum representation learning for reliable classification. The proposed framework incorporates an Adaptive Feature Confidence Module (AFCM) to estimate the contribution of individual input features before quantum encoding and an Uncertainty Estimation Module (UEM) to characterize feature reliability during the learning process. The confidence-weighted features are encoded using a variational quantum circuit and subsequently processed by an enhanced multi-layer classical classifier. Benchmark datasets, including MNIST, Fashion-MNIST, and PathMNIST, are employed during data preprocessing and quantum feature generation to establish the proposed framework. The detailed experimental evaluation, performance analysis, and comparative results presented in this study are conducted on the MNIST benchmark dataset to provide a comprehensive assessment of the proposed architecture. A systematic investigation using 4-, 6-, and 8-qubit hybrid models examines the influence of quantum feature dimensionality on classification performance. The final UVQFL model achieves a best validation accuracy of 84.62% , with 84.60% precision , 84.62% recall , and an 84.59% F1-score . The results indicate that integrating adaptive feature confidence with uncertainty-aware quantum feature learning improves feature representation quality and enhances the reliability of hybrid quantum–classical classification.
Accurate and computationally efficient classification of brain tumors from MRI scans is critical for deployment in resource-constrained clinical settings, where conventional deep CNN architectures-despite achieving strong accuracy-often require large parameter counts that limit scalability. This work proposes a hybrid quantum-classical deep learning framework that combines a classical convolutional feature extractor with a variational quantum circuit (VQC) for multi-class tumor classification. Spatial features extracted by the CNN backbone will be dimensionality-reduced and encoded into quantum states using angle encoding; a parameterized quantum circuit will then process these features in a higher-dimensional Hilbert space, leveraging superposition and entanglement to capture non-linear feature correlations. Measurement outputs from the quantum layer will be passed to a classical fully connected layer for classification into glioma, meningioma, pituitary tumor, and no-tumor categories. The proposed framework will be evaluated on a publicly available benchmark MRI dataset and benchmarked against state-of-the-art classical CNN architectures in terms of accuracy, precision, recall, F1-score, and parameter efficiency. This study aims to demonstrate that quantum-enhanced layers can achieve competitive classification performance while substantially reducing trainable parameters, offering a practical direction for efficient, quantum-assisted medical image analysis.
With the advancement of quantum computing in the Noisy Intermediate-Scale Quantum (NISQ) era, hybrid quantum-classical algorithms have emerged as promising approaches for addressing certain machine learning tasks. This study investigates the Variational Quantum Classifier (VQC), a hybrid model that integrates parameterized quantum circuits with classical optimization routines, implemented via IBM’s Qiskit framework. We provide a detailed account of the VQC architecture, including feature encoding, ansatz construction, and optimization methodology. To assess its performance, we conduct a comparative analysis between VQC and classical Support Vector Classifiers (SVC) across synthetic and real-world datasets, including Iris and a binary-filtered version of MNIST. Experimental results reveal that classical models consistently achieve higher accuracy and faster training times. However, the VQC demonstrates functional viability, particularly under noise-free conditions and reduced-dimensionality settings. These findings underscore the current limitations of quantum models while highlighting the potential of VQC as a proof-of-concept for quantum-enhanced learning as quantum hardware matures.
Abstract We introduce a hybrid model combining a quantum-inspired tensor network and a variational quantum circuit to perform supervised learning tasks. This architecture allows for the classical and quantum parts of the model to be trained simultaneously, providing an end-to-end training framework. We show that compared to the principal component analysis, a tensor network based on the matrix product state with low bond dimensions performs better as a feature extractor for the input data of the variational quantum circuit in the binary and ternary classification of MNIST and Fashion-MNIST datasets. The architecture is highly adaptable and the classical-quantum boundary can be adjusted according to the availability of the quantum resource by exploiting the correspondence between tensor networks and quantum circuits.