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IEEE International Conference on Healthcare Informatics (ICHI)

Reinforcement Learning-Guided Adaptive Fusion Model: Hybrid-Parametrized Quantum Circuits for Robust Histopathological Tumor Classification

Md Jobair Hossain Faruk, Md Kamrul Siam, Jerry Q. Cheng, Huanying Gu, Louis P. Dehner, Mai He

Published Jun 1, 2026• 1 citation
Publisher
DOI
10.1109/ichi69079.2026.00081

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Brain tumor classification remains a challenging task due to the complex heterogeneity of glioma characteristics and the limitations of conventional machine learning approaches in effectively integrating multimodal biomedical data. Recent advances in quantum machine learning have demonstrated promising capabilities for handling high-dimensional data; however, existing models often suffer from limited feature representation, insufficient multimodal integration, and optimization challenges. This paper proposes an Enhanced Hybrid Quantum–Classical Architecture (EHQCA) for glioma classification by combining MRI imaging features and TCGA molecular biomarkers within a unified learning framework. The proposed methodology employs ResNet-50 for imaging feature extraction, ensemble feature selection for molecular biomarkers, attention-based multimodal fusion, adaptive amplitude encoding, and a Multi-Layer Variational Quantum Classifier (ML-VQC). A hybrid Adam-SPSA optimization strategy is introduced to improve convergence speed and training stability. Experimental analysis demonstrates that the proposed EHQCA framework achieves superior classification performance, attaining 91.2% accuracy while reducing training epochs compared with existing quantum and classical models. The results highlight the effectiveness of multimodal fusion and quantum-enhanced learning for improving brain tumor diagnosis and supporting precision medicine applications
Diffusion models typically employ static or heuristic classifier-free guidance (CFG) schedules, which often fail to adapt across timesteps and noise conditions. In this work, we introduce a quantum reinforcement learning (QRL) controller that dynamically adjusts CFG at each denoising step. The controller adopts a hybrid quantum-classical actor-critic architecture: a shallow variational quantum circuit (VQC) with ring entanglement generates policy features, which are mapped by a compact multilayer perceptron (MLP) into Gaussian actions over ΔCFG, while a classical critic estimates value functions. The policy is optimized using Proximal Policy Optimization (PPO) with Generalized Advantage Estimation (GAE), guided by a reward that balances classification confidence, perceptual improvement, and action regularization. Experiments on CIFAR-10 demonstrate that our QRL policy improves perceptual quality (LPIPS, PSNR, SSIM) while reducing parameter count compared to classical RL actors and fixed schedules. Ablation studies on qubit number and circuit depth reveal trade-offs between accuracy and efficiency, and extended evaluations confirm robust generation under long diffusion schedules.