Quantum Support Vector Machine (Quantum SVM) is a prominent algorithm in quantum machine learning. A key challenge in this field is the efficient mapping of classical data into a quantum feature space. Despite its significance, there is a lack of systematic studies identifying the most suitable feature map for remote sensing specfic classification tasks. This study bridges that gap by determining the optimal feature map for forest class identification, revealing the superior potential of the Pauli feature map in remote sensing data classification. By leveraging Pauli feature representations, Quantum SVMs enable more effective extraction of quantum-enhanced patterns from complex datasets, providing a robust framework for solving non-linear classification problems in quantum machine learning.
Quantum Support Vector Machine (Quantum SVM) is a prominent algorithm in quantum machine learning. A key challenge in this field is the efficient mapping of classical data into a quantum feature space. Despite its significance, there is a lack of systematic studies identifying the most suitable feature map for remote sensing specfic classification tasks. This study bridges that gap by determining the optimal feature map for forest class identification, revealing the superior potential of the Pauli feature map in remote sensing data classification. By leveraging Pauli feature representations, Quantum SVMs enable more effective extraction of quantum-enhanced patterns from complex datasets, providing a robust framework for solving non-linear classification problems in quantum machine learning.
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 PCA (Principal Component Analysis). 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’s performance we compared it with classical ML such as Support Vector Machine (SVM) and Random Forest (RF) using 5-fold stratified cross-validation for a comprehensive evaluation. The experimental results demonstrate that the Z-feature map-based QSVM outperforms classical models achieving an accuracy of 99.23% surpassing both SVM and RF models.This research highlights the advantages of integrating quantum computing into image classification and also provides disease detection solution into the potential of hybrid quantum-classical model.
Abstract The study proposes a mechanism to generate effective feature maps with optimal feature selection using the Tabu Search algorithm. It compares the performance of classical support vector machines (SVM), quantum support vector machines (QSVM) with only gate selection, and QSVM with both gate selection and feature selection (QSVM-FS) across various datasets. The results indicate that classical SVMs excel with several benchmark datasets, while QSVMs show superior performance on synthetic datasets with non-linear separability. Notably, QSVM-FS consistently outperforms QSVM without feature selection, highlighting the importance of feature selection in enhancing model accuracy. These findings suggest that while both quantum and classical SVMs have unique advantages, quantum methods offer particular benefits in specific scenarios. In the NISQ era, classical simulations are a primary tool for assessing quantum experiments, though they face challenges such as design impacts, limited scales, and biases. Ultimately, no definitive winner exists between quantum and classical methods, as both have their own strengths.
The essence of quantum machine learning is to optimize problem-solving by executing machine learning algorithms on quantum computers and exploiting potent laws such as superposition and entanglement. Support vector machine (SVM) is widely recognized as one of the most effective classification machine learning techniques currently available. Since, in conventional systems, the SVM kernel technique tends to sluggish down and even fail as datasets become increasingly complex or jumbled. To compare the execution time and accuracy of conventional SVM classification to that of quantum SVM classification, the appropriate quantum features for mapping need to be selected. As the dataset grows complex, the importance of selecting an appropriate feature map that outperforms or performs as well as the classification grows. This paper utilizes conventional SVM to select an optimal feature map and benchmark dataset for predicting air quality. Experimental evidence demonstrates that the precision of quantum SVM surpasses that of classical SVM for air quality assessment. Using quantum labs from IBM's quantum computer cloud, conventional and quantum computing have been compared. When applied to the same dataset, the conventional SVM achieved an accuracy of 91% and 87% respectively, whereas the quantum SVM demonstrated an accuracy of 97% and 94% respectively for air quality prediction. The study introduces the use of quantum Support Vector Machines (SVM) for predicting air quality. It emphasizes the novel method of choosing the best quantum feature maps. Through the utilization of quantum-enhanced feature mapping, our objective is to exceed the constraints of classical SVM and achieve unparalleled levels of precision and effectiveness. We conduct precise experiments utilizing IBM's state-of-the-art quantum computer cloud to compare the performance of conventional and quantum SVM algorithms on a shared dataset.
Background: Classical algorithms often struggle with the high dimensionality of medical data critical for early lung cancer diagnosis. While Quantum Machine Learning (QML) offers enhanced pattern recognition capabilities, the impact of specific quantum feature encoding strategies on diagnostic accuracy remains underexplored.Methods: We evaluated Quantum Support Vector Machines (QSVM) using a dataset of 309 lung cancer patients, divided into six balanced subsets to mitigate class imbalance. Models were implemented on a qasm simulator, comparing three encoding strategies: ZFeatureMap, ZZFeatureMap, and PauliFeatureMap. Performance was assessed using standard classification metrics.Results: The choice of feature map significantly influenced model efficacy. The PauliFeatureMap outperformed other kernels, achieving 100% classification accuracy in three of the six subsets, whereas ZFeatureMap and ZZFeatureMap yielded lower predictive consistency.Conclusion: Quantum feature map selection is a decisive factor in QSVM performance. Specifically, the PauliFeatureMap demonstrates high separability for medical data, highlighting the potential of optimized quantum kernels to improve diagnostic precision.
Image classification has become a fundamental task in computer vision with applications in areas such as medical imaging, agriculture, environmental monitoring, and automated surveillance. Traditional machine learning techniques have achieved reasonable performance in classification tasks; however, they often struggle when dealing with high-dimensional and complex image datasets. Deep learning models, particularly Convolutional Neural Networks (CNNs), have significantly improved image classification performance by automatically learning hierarchical feature representations. Despite these advancements, classical deep learning models may still face challenges related to computational complexity and large-scale data processing.In recent years, quantum machine learning has emerged as a promising paradigm that combines principles of quantum computing with classical machine learning techniques to enhance computational efficiency and model performance. This study proposes a hybrid quantum–classical framework for image classification that integrates a deep residual network (ResNet-50) with a Quantum Support Vector Machine (QSVM). The ResNet-50 model is employed as a feature extraction mechanism to capture high-level visual representations from image data. The extracted features are then reduced in dimensionality using Principal Component Analysis (PCA) to simplify the feature space and improve computational efficiency.The reduced feature vectors are subsequently classified using a QSVM model that utilizes quantum feature maps to encode classical data into quantum states. Various quantum feature maps are explored to evaluate their impact on classification performance. Experimental results demonstrate that the hybrid quantum–classical approach achieves higher classification accuracy compared to conventional machine learning models such as Support Vector Machines and Random Forest classifiers. The proposed framework highlights the potential of combining classical deep learning architectures with quantum machine learning algorithms to address complex image classification challenges. This hybrid approach provides an efficient and scalable solution for advanced image analysis tasks and demonstrates the growing potential of quantum computing in artificial intelligence applications.
Abstract This work investigates the feasibility of using quantum machine learning for reforestation-suitability classification in the Mumbai region by integrating Quantum Support Vector Machines (QSVMs) with a GIS-based preprocessing pipeline. Satellite-derived indicators---NDVI, rainfall, slope, soil pH, and soil organic carbon---are extracted from Google Earth Engine and labeled using rule-based ecological thresholds. The resulting dataset is standardized, scaled to [−1,1], balanced with SMOTE, and compressed using Principal Component Analysis (PCA) to align the feature dimension with the number of qubits. Two quantum kernel models, based on Pauli and ZZ feature maps, are implemented in Qiskit and evaluated against classical SVM baselines under identical preprocessing conditions. On the balanced dataset, the classical SVM achieves an accuracy of approximately 98\%, while Pauli- and ZZ-based QSVMs obtain 89\% and 77\% accuracy, respectively. These results indicate that, although quantum models do not surpass classical SVMs in this setting, well-designed quantum kernels can achieve competitive performance on small, structured geospatial datasets. The study positions QSVMs as complementary tools rather than replacements for classical models and highlights the importance of scaling, class balancing, and PCA--qubit alignment for stable quantum behavior in environmental analytics.