Feature-Adaptive Fusion in Hybrid Quantum-Classical Neural Networks for Robust Biomedical Image Classification

• URL: http://arxiv.org/abs/2609.15267v1
• Date: Mon, 14 Sep 2026 09:26:39 GMT
• Title: Feature-Adaptive Fusion in Hybrid Quantum-Classical Neural Networks for Robust Biomedical Image Classification
• Abstract summary: We propose a Feature-Adaptive Fusion Hybrid Quantum-Classical Neural Network (FAF-HQNN) for biomedical image classification.<n>We evaluate FAF-HQNN on two MedMNIST benchmarks, PathMNIST and BloodMNIST, under clean and corrupted test conditions.
• Score: 5.648121717707402
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hybrid quantum-classical neural networks provide a promising approach for incorporating quantum circuits into machine learning in the noisy intermediate-scale quantum regime. However, existing hybrid models often rely on fixed or globally shared fusion strategies, which may limit their ability to exploit complementary information carried by quantum branches, especially under distribution shifts. In this work, we propose a Feature-Adaptive Fusion Hybrid Quantum-Classical Neural Network (FAF-HQNN) for biomedical image classification. The model combines a classical deep feature encoder with a variational quantum circuit (VQC) and introduces a feature-adaptive fusion mechanism to dynamically weight classical and quantum predictions. We evaluate FAF-HQNN on two MedMNIST benchmarks, PathMNIST and BloodMNIST, under clean and corrupted test conditions. FAF-HQNN achieves the strongest overall performance on clean data among the compared methods and shows improved robustness under Gaussian, salt-and-pepper, and Poisson corruptions. Further analysis of circuit layout, measurement basis, and depth shows that even shallow variational quantum circuits can provide useful complementary information. These results demonstrate that feature-adaptive fusion is an effective strategy for improving accuracy and robustness in hybrid quantum-classical models for biomedical image classification.

Related papers

• A hybrid quantum-classical neural network for learning to route [36.95397691288925]
  This work studies hybrid quantum-classical neural networks for learning routings.<n>For the capacitated vehicle routing problem, encoder feed-forward replacement emerges as the most promising design.
  arXiv  Detail & Related papers  (2026-08-31T23:36:36Z)
• Do emulated quantum circuits change what CNNs look at? Performance and explainability comparison in medical image classification [0.879504058268139]
  We investigate whether small, classically-emulated quantum circuit components can play a meaningful role within complex models.<n>We present a systematic study of the effectiveness of a Hybrid Quantum-inspired Convolutional Neural Network (HQiCNN) compared with a parameter-matched classical Convolutional Neural Network (CNN)<n>The results show that no architecture consistently dominates the other: the HQiCNN achieves its largest gains in intermediate-data regimes, whereas the CNN reaches the highest accuracies for the largest training sets in both datasets.
  arXiv  Detail & Related papers  (2026-07-23T11:15:35Z)
• On the Complementarity of Quantum and Classical Features: Adaptive Hybrid Quantum-Classical Feature Fusion for Breast Cancer Classification [1.4856472820492366]
  The integration of quantum machine learning with classical deep learning offers promising avenues for medical image analysis.<n>We propose a novel hybrid quantum-classical architecture for breast cancer diagnosis based on a dual-branch feature-extraction pipeline.<n>Our framework extracts and unifies complementary representations from classical models and quantum circuits.
  arXiv  Detail & Related papers  (2026-04-24T14:54:53Z)
• Hybrid Quantum Neural Networks for Enhanced Breast Cancer Thermographic Classification: A Novel Quantum-Classical Integration Approach [0.0]
  Breast cancer diagnosis through thermographic image analysis remains a critical challenge in medical AI.<n>This paper presents a novel Hybrid Quantum Neural Network architecture that integrates quantum computing principles with classical convolutional neural networks for enhanced breast cancer classification.<n>Our findings provide evidence for quantum advantage in medical image classification through classical simulation, establishing a framework for quantum-classical hybrid systems in healthcare applications.
  arXiv  Detail & Related papers  (2026-04-18T10:24:51Z)
• Quantum Implicit Neural Representations for 3D Scene Reconstruction and Novel View Synthesis [42.13843953705695]
  Implicit neural representations (INRs) have become a powerful paradigm for continuous signal modeling and 3D scene reconstruction.<n>We present Quantum Neural Radiance Fields (Q-NeRF), the first hybrid quantum-classical framework for neural radiance field rendering.
  arXiv  Detail & Related papers  (2025-12-14T13:24:11Z)
• Parallel Multi-Circuit Quantum Feature Fusion in Hybrid Quantum-Classical Convolutional Neural Networks for Breast Tumor Classification [0.0]
  We present a hybrid Quantum-Classical Convolutional Neural Network (QCNN) architecture designed for the binary classification of the BreastMNIST dataset.<n>Our results indicate that hybrid QCNN architectures can leverage entanglement and quantum feature fusion to enhance medical image classification tasks.
  arXiv  Detail & Related papers  (2025-11-29T17:47:14Z)
• Towards Quantum Enhanced Adversarial Robustness with Rydberg Reservoir Learning [45.92935470813908]
  Quantum computing reservoir (QRC) leverages the high-dimensional, nonlinear dynamics inherent in quantum many-body systems.<n>Recent studies indicate that perturbation quantums based on variational circuits remain susceptible to adversarials.<n>We investigate the first systematic evaluation of adversarial robustness in a QR based learning model.
  arXiv  Detail & Related papers  (2025-10-15T12:17:23Z)
• HQCNN: A Hybrid Quantum-Classical Neural Network for Medical Image Classification [0.0]
  We propose a novel Hybrid Quantum-Classical Neural Network (HQCNN) for both binary and multi-class classification.<n>The architecture of HQCNN integrates a five-layer classical convolutional backbone with a 4-qubit variational quantum circuit.<n>We evaluate the model on six MedMNIST v2 benchmark datasets.
  arXiv  Detail & Related papers  (2025-09-16T08:02:53Z)
• VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [50.95799256262098]
  Variational quantum circuits (VQCs) hold promise for quantum machine learning but face challenges in expressivity, trainability, and noise resilience.<n>We propose VQC-MLPNet, a hybrid architecture where a VQC generates the first-layer weights of a classical multilayer perceptron during training, while inference is performed entirely classically.
  arXiv  Detail & Related papers  (2025-06-12T01:38:15Z)
• Towards Efficient Quantum Hybrid Diffusion Models [68.43405413443175]
  We propose a new methodology to design quantum hybrid diffusion models. We propose two possible hybridization schemes combining quantum computing's superior generalization with classical networks' modularity.
  arXiv  Detail & Related papers  (2024-02-25T16:57:51Z)
• Quantum machine learning for image classification [39.58317527488534]
  This research introduces two quantum machine learning models that leverage the principles of quantum mechanics for effective computations. Our first model, a hybrid quantum neural network with parallel quantum circuits, enables the execution of computations even in the noisy intermediate-scale quantum era. A second model introduces a hybrid quantum neural network with a Quanvolutional layer, reducing image resolution via a convolution process.
  arXiv  Detail & Related papers  (2023-04-18T18:23:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.