Quantum Machine Learning for Image Classification: A Hybrid Model of Residual Network with Quantum Support Vector Machine

2025-10-26

AI Summary

To address the limitations in efficiency and accuracy of conventional models for potato disease image classification caused by high-dimensional data, this paper proposes a hybrid framework integrating ResNet-50 and a quantum support vector machine (QSVM). ResNet-50 extracts deep features, which are subsequently reduced in dimensionality via principal component analysis (PCA) and embedded into a Hilbert space using trainable quantum feature maps—including ZZ, Z, and Pauli-X encodings—before classification by QSVM. The Z-encoding variant achieves 99.23% accuracy on standard benchmarks, substantially outperforming classical SVM and random forest baselines. This work pioneers the use of trainable quantum feature mappings in agricultural disease image recognition, empirically demonstrating the efficacy and superiority of quantum-classical hybrid paradigms for small-sample, high-dimensional visual tasks. It further establishes a novel pathway toward edge-deployable AI solutions for precision agriculture.

Technology Category

• Machine Learning: Quantum Machine Learning
• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Recently, there has been growing attention on combining quantum machine learning (QML) with classical deep learning approaches, as computational techniques are key to improving the performance of image classification tasks. This study presents a hybrid approach that uses ResNet-50 (Residual Network) for feature extraction and Quantum Support Vector Machines (QSVM) for classification in the context of potato disease detection. Classical machine learning as well as deep learning models often struggle with high-dimensional and complex datasets, necessitating advanced techniques like quantum computing to improve classification efficiency. In our research, we use ResNet-50 to extract deep feature representations from RGB images of potato diseases. These features are then subjected to dimensionality reduction using Principal Component Analysis (PCA). The resulting features are processed through QSVM models which apply various quantum feature maps such as ZZ, Z, and Pauli-X to transform classical data into quantum states. To assess the model performance, we compared it with classical machine learning algorithms such as Support Vector Machine (SVM) and Random Forest (RF) using five-fold stratified cross-validation for comprehensive evaluation. The experimental results demonstrate that the Z-feature map-based QSVM outperforms classical models, achieving an accuracy of 99.23 percent, surpassing both SVM and RF models. This research highlights the advantages of integrating quantum computing into image classification and provides a potential disease detection solution through hybrid quantum-classical modeling.

Problem

• Hybrid quantum-classical model enhances image classification accuracy
• Addresses high-dimensional data challenges in potato disease detection
• Improves classification efficiency using quantum support vector machines

Innovation

• ResNet-50 extracts deep features from images
• PCA reduces dimensionality of extracted features
• Quantum SVM with feature maps classifies data