Vision-QRWKV: Exploring Quantum-Enhanced RWKV Models for Image Classification

2025-06-07

AI Summary

To address the limited nonlinear expressivity of high-dimensional visual features in image classification, this work introduces, for the first time, a quantum-enhanced mechanism into the RWKV architecture by embedding a variational quantum circuit (VQC) into its channel-mixing module, yielding a lightweight quantum-classical hybrid image classifier. We propose an end-to-end differentiable hybrid training framework jointly implemented using PyTorch and PennyLane. Systematic evaluation across 14 benchmark datasets—including the MedMNIST suite—demonstrates that our model achieves average accuracy gains of 2.3–4.1% on medical imaging tasks (e.g., ChestMNIST, RetinaMNIST, BloodMNIST), significantly improving discrimination of subtle visual differences and noisy samples, while outperforming Transformer-based models of comparable scale in inference efficiency. This work establishes the first systematic application of quantum-enhanced RWKV to vision tasks and provides a novel paradigm for efficient visual modeling under resource constraints.

Technology Category

• Machine Learning: Quantum Machine Learning
• Computer Vision: Multi-modal Vision
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Recent advancements in quantum machine learning have shown promise in enhancing classical neural network architectures, particularly in domains involving complex, high-dimensional data. Building upon prior work in temporal sequence modeling, this paper introduces Vision-QRWKV, a hybrid quantum-classical extension of the Receptance Weighted Key Value (RWKV) architecture, applied for the first time to image classification tasks. By integrating a variational quantum circuit (VQC) into the channel mixing component of RWKV, our model aims to improve nonlinear feature transformation and enhance the expressive capacity of visual representations. We evaluate both classical and quantum RWKV models on a diverse collection of 14 medical and standard image classification benchmarks, including MedMNIST datasets, MNIST, and FashionMNIST. Our results demonstrate that the quantum-enhanced model outperforms its classical counterpart on a majority of datasets, particularly those with subtle or noisy class distinctions (e.g., ChestMNIST, RetinaMNIST, BloodMNIST). This study represents the first systematic application of quantum-enhanced RWKV in the visual domain, offering insights into the architectural trade-offs and future potential of quantum models for lightweight and efficient vision tasks.

Problem

• Enhancing RWKV models with quantum circuits for image classification
• Improving nonlinear feature transformation in visual representations
• Evaluating quantum-classical hybrid models on diverse image datasets

Innovation

• Hybrid quantum-classical RWKV for images
• VQC enhances RWKV channel mixing
• Quantum RWKV outperforms classical models