Quantum Support Vector Machine Research Articles

A hybrid quantum ensemble learning model for malicious code detection

Quantum computing as a new computing model with parallel computing capability and high information carrying capacity, has attracted a lot of attention from researchers. Ensemble learning is an effective strategy often used in machine learning to improve the performance of weak classifiers. Currently, the classification performance of quantum classifiers is not satisfactory enough due to factors such as the depth of quantum circuit, quantum noise, and quantum coding method, etc. For this reason, this paper combined the ensemble learning idea and quantum classifiers to design a novel hybrid quantum machine learning model. Firstly, we run the Stacking method in classical machine learning to realize the dimensionality reduction of high-latitude data while ensuring the validity of data features. Secondly, we used the Bagging method and Bayesian hyperparameter optimization method applied to quantum support vector machine (QSVM), quantum K nearest neighbors (QKNN), variational quantum classifier (VQC). Thirdly, the voting method is used to ensemble the predict results of QSVM, QKNN, VQC as the final result. We applied the hybrid quantum ensemble machine learning model to malicious code detection. The experimental results show that the classification precision (accuracy, F1-score) of this model has been improved to 98.9% (94.5%, 94.24%). Combined with the acceleration of quantum computing and the higher precision rate, it can effectively deal with the growing trend of malicious codes, which is of great significance to cyberspace security.

A novel intrusion detection system based on a hybrid quantum support vector machine and improved Grey Wolf optimizer

AbstractThe Internet of Things (IoT) has grown significantly in recent years, allowing devices with sensors to share data via the internet. Despite the growing popularity of IoT devices, they remain vulnerable to cyber-attacks. To address this issue, researchers have proposed the Hybrid Intrusion Detection System (HIDS) as a way to enhance the security of IoT. This paper presents a novel intrusion detection model, namely QSVM-IGWO, for improving the detection capabilities and reducing false positive alarms of HIDS. This model aims to improve the performance of the Quantum Support Vector Machine (QSVM) by incorporating parameters from the Improved Grey Wolf Optimizer (IGWO) algorithm. IGWO is introduced under the hypothesis that the social hierarchy observed in grey wolves enhances the searching procedure and overcomes the limitations of GWO. In addition, the QSVM model is employed for binary classification by selecting the kernel function to obtain an optimal solution. Experimental results show promising performance of QSVM-IGWO in terms of accuracy, Recall, Precision, F1 score, and ROC curve, when compared with recent detection models.

Permutation invariant encodings for quantum machine learning with point cloud data

Quantum computing offers a potentially powerful new method for performing machine learning. However, several quantum machine learning techniques have been shown to exhibit poor generalisation as the number of qubits increases. We address this issue by demonstrating a permutation invariant quantum encoding method, which exhibits superior generalisation performance, and apply it to point cloud data (three-dimensional images composed of points). Point clouds naturally contain permutation symmetry with respect to the ordering of their points, making them a natural candidate for this technique. Our method captures this symmetry in a quantum encoding that contains an equal quantum superposition of all permutations and is therefore invariant under point order permutation. We test this encoding method in numerical simulations using a quantum support vector machine to classify point clouds drawn from either spherical or toroidal geometries. We show that a permutation invariant encoding improves in accuracy as the number of points contained in the point cloud increases, while non-invariant quantum encodings decrease in accuracy. This demonstrates that by implementing permutation invariance into the encoding, the model exhibits improved generalisation.

Cloud computing-based framework for heart disease classification using quantum machine learning approach

Abstract Accurate early identification and treatment of cardiovascular diseases can prevent heart failure problems and reduce mortality rates. This study aims to use quantum learning to predict heart problems to increase the accuracy of traditional prediction and classification methods. Machine learning (ML) and deep learning (DL) techniques need quantum learning to quickly and accurately analyze massive volumes of complex data. With quantum computing, the suggested DL and ML algorithms can change their predictions on the basis of changes in the dataset. This approach could help with the early and accurate detection of chronic diseases. The Cleveland heart disease dataset is undergoing preliminary processing to validate missing values to increase the precision rate and prevent incorrect forecasts. This study examined the feasibility of employing and deploying a quantum ML (QML) framework via cloud computing to categorize cardiac conditions. The research was divided into four sections. First, the principal component analysis was used to preprocess the Cleveland dataset, recursive feature elimination was used to select features, and min–max normalization was used to give the dataset a high-dimensional value. Second, we compared traditional classifiers, such as support vector machine (SVM) and artificial neural network, with the quantum approach to verify the quantum approach’s efficiency. Third, we examined two unique QML classification methods: quantum neural networks (QNNs) and quantum SVM (QSVM). Fourth, bagging-QSVM was developed and deployed as an ensemble learning model. Experimental results using the QNN show an accuracy of 77%, a precision of 76%, a recall of 73%, and an F1 score of 75%. With an accuracy of 85%, a precision of 79%, a recall of 90%, and an F1-score of 84%, the QSVM method demonstrated a much better performance than the QNN. Particularly, the Bagging_QSVM model exhibited an outstanding performance, with a flawless score of 100% across all critical performance measures. The study shows that the bagging method for ensemble learning is a solid way of increasing the accuracy of quantum method predictions.

A novel feature selection method based on quantum support vector machine

Feature selection is critical in machine learning to reduce dimensionality and improve model accuracy and efficiency. The exponential growth in feature space dimensionality for modern datasets directly results in ambiguous samples and redundant features, which can severely degrade classification accuracy. Quantum machine learning offers potential advantages for addressing this challenge. In this paper, we propose a novel method, quantum support vector machine feature selection (QSVMF), integrating quantum support vector machines with multi-objective genetic algorithm. QSVMF optimizes multiple simultaneous objectives: maximizing classification accuracy, minimizing selected features and quantum circuit costs, and reducing feature covariance. We apply QSVMF for feature selection on a breast cancer dataset, comparing the performance of QSVMF against classical approaches with the selected features. Experimental results show that QSVMF achieves superior performance. Furthermore, the Pareto front solutions of QSVMF enable analysis of accuracy versus feature set size trade-offs, identifying extremely sparse yet accurate feature subsets. We contextualize the biological relevance of the selected features in terms of known breast cancer biomarkers. This work highlights the potential of quantum-based feature selection to enhance machine learning efficiency and performance on complex real-world data.

Quantum Reversible Audio Steganalysis Using Quantum Schmidt Decomposition and Quantum Support Vector Machine

With the development of various methods of quantum steganography, the discovery of these secret communications has become a necessity. This paper presents the first quantum steganalysis approach that uses statistical features of the quantum frequency domain to detect the steganographic methods in quantum communication networks. In this method, by proposing different quantum circuits, statistical features such as quantum mean, quantum square mean root, quantum variance, and quantum standard deviation are extracted to increase the accuracy of distinguishing between stego and clean signals. In the proposed method, the Schmidt coefficient vector extracted from the Quantum Haar Wavelet Transform (QHWT) is used to calculate the statistical features of the audio signal so that the main and dominant coefficients below the frequency sub-bands of the audio signal can be identified and selected. The proposed method uses a Quantum Support Vector Machine (QSVM) to classify the extracted feature vectors. The higher accuracy of the proposed frequency-based steganalysis method indicates its higher efficiency in detecting stego and clean signals than the quantum steganalysis methods based on the time domain.

Quantum Computing in Machine Learning - The Future of Quantum Computing

In order to solve challenging optimization issues, analyze data effectively, and improve the capabilities of current machine learning algorithms, quantum computing has the potential to revolutionize the area of machine learning. The proposed work examines the fundamental ideas and methods of quantum computing—including quantum gates, quantum circuits, and quantum algorithms—as they relate to machine learning in this abstract. Various quantum computing applications in machine learning, including quantum neural networks, quantum support vector machines, and conventional methods influenced by quantum mechanics are also discussed. A review of state-of-the-art in quantum computing for machine learning, including recent advancements in quantum hardware and software has been done and the future prospects of this fascinating area has been examined.

Quantum Machine-Based Decision Support System for the Detection of Schizophrenia from EEG Records

Schizophrenia is a serious chronic mental disorder that significantly affects daily life. Electroencephalography (EEG), a method used to measure mental activities in the brain, is among the techniques employed in the diagnosis of schizophrenia. The symptoms of the disease typically begin in childhood and become more pronounced as one grows older. However, it can be managed with specific treatments. Computer-aided methods can be used to achieve an early diagnosis of this illness. In this study, various machine learning algorithms and the emerging technology of quantum-based machine learning algorithm were used to detect schizophrenia using EEG signals. The principal component analysis (PCA) method was applied to process the obtained data in quantum systems. The data, which were reduced in dimensionality, were transformed into qubit form using various feature maps and provided as input to the Quantum Support Vector Machine (QSVM) algorithm. Thus, the QSVM algorithm was applied using different qubit numbers and different circuits in addition to classical machine learning algorithms. All analyses were conducted in the simulator environment of the IBM Quantum Platform. In the classification of this EEG dataset, it is evident that the QSVM algorithm demonstrated superior performance with a 100% success rate when using Pauli X and Pauli Z feature maps. This study serves as proof that quantum machine learning algorithms can be effectively utilized in the field of healthcare.

AUTOMATED DETECTION OF PARKINSON’S DISEASE BASED ON HYBRID CNN AND QUANTUM MACHINE LEARNING TECHNIQUES IN MRI IMAGES

Parkinson’s disease (PD) is a long-term neurological condition that causes severe neuronal degeneration in the motor cortex. Because of the constraints of medical applications determining the severity of PD, forecasting its development can be difficult. Recently research has focused on artificial intelligence to automatically diagnose PD from MRI images. The proposed study aims (a) to incorporate automated method for augmentation of the MRI images using the deep convolutional generative adversarial networks (DCGAN), (b) to perform feature extraction and classification by employing the pre-trained models such as VGG16, Xception, and InceptionV3 models, (c) to develop a hybrid model by fusing the InceptionV3 features and classification using QSVM model. A total of 60 real-time MRIs of normal (N = 30) and PD (N = 30) patients were included in this study. To increase the dataset, the proposed study employed the DCGAN approach. Thus, the dataset for this study was increased to 1000 (normal and PD) images. Automatic feature extraction and classification were performed utilizing different pre-trained models such as VGG16, Xception, and InceptionV3. Among the pre-trained models InceptionV3 architecture provided high accuracy of 74% compared to other models. The proposed work developed a hybrid model by fusing InceptionV3 features and a quantum support vector machine (QSVM) for the detection of PD and healthy groups. The proposed model attained a prediction accuracy of 87.5% and high precision of 95%, respectively. Additionally, the hybrid architecture provided high recall and F1-measure of 84% and 89%, respectively. The hybrid model provided lowest false negative and false positive of 4 and 1, respectively. Therefore, the hybrid model could be an effective diagnostic tool for the automated prediction of PD.

Hybrid Quantum Technologies for Quantum Support Vector Machines

Quantum computing has rapidly gained prominence for its unprecedented computational efficiency in solving specific problems when compared to classical computing counterparts. This surge in attention is particularly pronounced in the realm of quantum machine learning (QML) following a classical trend. Here we start with a comprehensive overview of the current state-of-the-art in Quantum Support Vector Machines (QSVMs). Subsequently, we analyze the limitations inherent in both annealing and gate-based techniques. To address these identified weaknesses, we propose a novel hybrid methodology that integrates aspects of both techniques, thereby mitigating several individual drawbacks while keeping the advantages. We provide a detailed presentation of the two components of our hybrid models, accompanied by the presentation of experimental results that corroborate the efficacy of the proposed architecture. These results pave the way for a more integrated paradigm in quantum machine learning and quantum computing at large, transcending traditional compartmentalization.

On neural quantum support vector machines

On neural quantum support vector machines

Cucumber Leaf Disease Detection using GLCM Features with Random Forest Algorithm

Agriculture plays a vital role in India's economy, and the health of crops is critical for maximizing yield. In particular, cucumber, a key salad ingredient known for its health benefits, is susceptible to various diseases such as water mold, bacterial wilt, angular leaf spot, anthracnose, and powdery mildew. These diseases not only affect the quality of cucumbers but also significantly reduce their yield. Early detection of these diseases is crucial for successful cultivation, but traditional manual methods of disease identification by farmers or diagnosticians are time-consuming and prone to misidentification. To address these challenges, we explore advanced artificial intelligence techniques. We implement and compare various machine learning algorithms, including ResNet, AlexNet, and VGG-16, for disease classification in cucumbers. However, these methods often struggle with issues such as noise, irrelevant features, and the generation of pertinent characteristics. To overcome these limitations, we propose a novel approach using a GLCM (Gray Level Co-occurrence Matrix) feature extraction method combined with a Random Forest classifier. This new algorithm aims to improve the accuracy and efficiency of disease detection. Our dataset comprises four distinct categories: Healthy, Anthracnose, Aphids, and CYSDV. It is sourced from diverse platforms, including online repositories like kaggle and direct collection from cucumber farms. The initial phase of our methodology involves noise reduction by converting images into the LAB color space and isolating specific regions using the k-means clustering algorithm. Subsequently, we extract texture features from the diseased leaf images using the GLCM algorithm, and classification is performed using the Random Forest model. Comparative analysis shows that our proposed Random Forest algorithm outperforms previous models like LGBM (Light Gradient Boosting Machine) and QSVM (Quantum-Support Vector Machine) in predicting disease presence in cucumber plants with higher accuracy rate of 98.62%, Precision 98.77%, Recall 98.48% and also F1 Score 98.62%.

The complexity of quantum support vector machines

Quantum support vector machines employ quantum circuits to define the kernel function. It has been shown that this approach offers a provable exponential speedup compared to any known classical algorithm for certain data sets. The training of such models corresponds to solving a convex optimization problem either via its primal or dual formulation. Due to the probabilistic nature of quantum mechanics, the training algorithms are affected by statistical uncertainty, which has a major impact on their complexity. We show that the dual problem can be solved in O(M4.67/ε2) quantum circuit evaluations, where M denotes the size of the data set and ε the solution accuracy compared to the ideal result from exact expectation values, which is only obtainable in theory. We prove under an empirically motivated assumption that the kernelized primal problem can alternatively be solved in O(min{M2/ε6,1/ε10}) evaluations by employing a generalization of a known classical algorithm called Pegasos. Accompanying empirical results demonstrate these analytical complexities to be essentially tight. In addition, we investigate a variational approximation to quantum support vector machines and show that their heuristic training achieves considerably better scaling in our experiments.

Enhancing Real-Time Malware Analysis with Quantum Neural Networks

The proposed Quantum Neural Networks (QNN) perform better than traditional machine learning models. The escalating complexity of malware poses a significant challenge to cybersecurity, necessitating innovative approaches to keep pace with its rapid evolution. Contemporary malware analysis techniques underscore the urgent need for solutions that can adapt to the dynamic functionalities of evolving malware. In this context, Quantum Neural Networks (QNNs) emerge as a cutting-edge and distinctive approach to malware analysis, promising to overcome the limitations of conventional methods. Our exploration of QNNs focuses on uncovering their valuable applications, particularly in real-time malware research. We meticulously examine the advantages of QNNs in contrast to conventional machine-learning methods employed in malware detection and classification. The proposed QNN showcases its unique capability to handle complex patterns, emphasizing its potential to achieve heightened levels of accuracy. Our contribution extends to introducing a dedicated framework for QNN-based malware analysis, harnessing the formidable computational capabilities of quantum computing for real-time malware analysis. This framework is structured around three pivotal components, Malware Feature Extraction utilizes quantum feature extraction techniques to identify relevant features from malware samples. Malware Classification employs a QNN classifier to categorize malware samples as benign or malicious. Real-Time Analysis enables the instantaneous examination of malware samples by integrating feature extraction and classification within a streaming data pipeline. Our proposed methodology undergoes comprehensive evaluation using a benchmark dataset of malware samples. The Proposed Quantum Neural Networks (QNNs) demonstrated a high accuracy of 0.95, outperforming other quantum models such as Quantum Support Vector Machines (QSVM) and Quantum Decision Trees (QDT), as well as classical models like Random Forest (RF), Support Vector Machines (SVM), and Decision Trees (DT) on the Malware DB dataset. The results affirm the framework's exceptional accuracy rates and low latency, establishing its suitability for real-time malware analysis. These findings underscore the potential for QNNs to revolutionize malware evaluation and strengthen real-time defenses against cyberattacks. While our research demonstrates promising outcomes, further exploration and development in this domain are imperative to fully exploit the extensive viability that QNNs offer for cybersecurity applications.

Quantum intelligence in medicine: Empowering thyroid disease prediction through advanced machine learning

AbstractThe medical information system is rich in datasets, but no intelligent systems can easily analyse the disease. Recently, ML (Machine Learning)‐based algorithms have acted as a handy diagnostic tool to identify whether a person is affected by thyroid or not. However, they produced classification with low accuracy and led to misclassification. Hence, the proposed system combines quantum computing with ML techniques to enhance computational power and precision. The system employs modified QPSO (Quantum Particle Swarm Optimisation) for feature selection since its searching performance is better than that of conventional PSO for selecting the optimum global position of the particle, thus selecting the relevant feature. Whereas, the QSVM (Quantum Support Vector Machine) is implemented for more accurate classification than classical SVM, as it tends to capture complex patterns in data produced due to high dimensional feature space applied by quantum kernel functions. This combination of modified QPSO and QSVM tends to increase the performance accuracy significantly. The efficiency of the proposed model is measured based on derivative parameters, such as F‐1‐score, recall, precision and accuracy, with corresponding confusion matrix and ROC. Further, the classification is compared with other traditional approaches to predict the accuracy of the proposed model with traditional methods.

A Hypertuned Lightweight and Scalable LSTM Model for Hybrid Network Intrusion Detection

Given the increasing frequency of network attacks, there is an urgent need for more effective network security measures. While traditional approaches such as firewalls and data encryption have been implemented, there is still room for improvement in their effectiveness. To effectively address this concern, it is essential to integrate Artificial Intelligence (AI)-based solutions into historical methods. However, AI-driven approaches often encounter challenges, including lower detection rates and the complexity of feature engineering requirements. Finding solutions to overcome these hurdles is critical for enhancing the effectiveness of intrusion detection systems. This research paper introduces a deep learning-based approach for network intrusion detection to overcome these challenges. The proposed approach utilizes various classification algorithms, including the AutoEncoder (AE), Long-short-term-memory (LSTM), Multi-Layer Perceptron (MLP), Linear Support Vector Machine (L-SVM), Quantum Support Vector Machine (Q-SVM), Linear Discriminant Analysis (LDA), and Quadratic Discriminant Analysis (QDA). To validate the effectiveness of the proposed approach, three datasets, namely IOT23, CICIDS2017, and NSL KDD, are used for experimentation. The results demonstrate impressive accuracy, particularly with the LSTM algorithm, achieving a 97.7% accuracy rate on the NSL KDD dataset, 99% accuracy rate on the CICIDS2017 dataset, and 98.7% accuracy on the IOT23 dataset. These findings highlight the potential of deep learning algorithms in enhancing network intrusion detection. By providing network administrators with robust security measures for accurate and timely intrusion detection, the proposed approach contributes to network safety and helps mitigate the impact of network attacks.

The role of entanglement for enhancing the efficiency of quantum kernels towards classification

Quantum kernels are considered as potential resources to illustrate benefits of quantum computing in machine learning. Considering the impact of hyperparameters on the performance of a classical machine learning model, it is imperative to identify promising hyperparameters using quantum kernel methods in order to achieve quantum advantages. In this work, we analyse and classify sentiments of textual data using a new quantum kernel based on linear and full entangled circuits as hyperparameters for controlling the correlation among words. We also find that the use of linear and full entanglement further controls the expressivity of the Quantum Support Vector Machine (QSVM). In addition, we also compare the efficiency of the proposed circuit with other quantum circuits and classical machine learning algorithms. Our results show that the proposed fully entangled circuit outperforms all other fully or linearly entangled circuits in addition to classical algorithms for most of the features. In fact, as the feature increases the efficiency of our proposed fully entangled model also increases significantly.

Brain Tumour Classification Using Quantum Support Vector Machine Learning Algorithm

Quantum computing is an emerging field that can effectively  solve several machine learning problems. The immensely growing data size has begun to create barriers for classical machine learning algorithms. Quantum computers proficiently handle and process big data composed of metrics and vectors. This paper presents the quantum machine learning model based on quantum support vector machines (QSVM) to classify brain tumours into malignant and benign. The dataset used in this experiment is Brats 2015, available on Kaggle. The QSVM-based model was run on real-time quantum machines and simulators to extract the performance metrics in terms of accuracy and execution time. The QSVM classification model is compared with its classical SVM equivalent. The kernel-based QSVM model implementation on a 32-qubit quantum simulator was 188 times faster with 95% accuracy which is 1.60% better than the classical equivalent. Similarly, the model takes 24.19% less time than the classical SVM model when realized on a 5-qubit real-time superconducting processor with the same accuracy. Hence, the results reveal that quantum machine learning models implemented on quantum computers outperformed their classical equivalents.

Boosted Ensembles of Qubit and Continuous Variable Quantum Support Vector Machines for B Meson Flavor Tagging

AbstractThe recent physical realization of quantum computers with hundreds of noisy qubits has given birth to an intense search for useful applications of their unique capabilities. One area that has received particular attention is quantum machine learning (QML), the study of machine learning algorithms running natively on quantum computers. In this work, QML methods are developed and applied to B meson flavor tagging, an important component of experiments which probe CP violation in order to better understand the matter‐antimatter asymmetry of the universe. One simulate boosted ensembles of quantum support vector machines (QSVMs) based on both conventional qubit‐based and continuous variable architectures, attaining effective tagging efficiencies of 28.0% and 29.2%, respectively, comparable with the leading published result of 30.0% using classical machine learning algorithms. The ensemble nature of the classifier is of particular importance, doubling the effective tagging efficiency of a single QSVM, which is find to be highly prone to overfitting. These results are obtained despite the constraint of working with QSVM architectures that are classically simulable, and it finds evidence that QSVMs beyond the simulable regime may be able to realize even higher performance, when sufficiently powerful quantum hardware is developed to execute them.

Cost-sensitive classification algorithm combining the Bayesian algorithm and quantum decision tree

This study highlights the drawbacks of current quantum classifiers that limit their efficiency and data processing capabilities in big data environments. The paper proposes a global decision tree paradigm to address these issues, focusing on designing a complete quantum decision tree classification algorithm that is accurate and efficient while also considering classification costs. The proposed method integrates the Bayesian algorithm and the quantum decision tree classification algorithm to handle incremental data. The proposed approach generates a suitable decision tree dynamically based on data objects and cost constraints. To handle incremental data, the Bayesian algorithm and quantum decision tree classification algorithm are integrated, and kernel functions obtained from quantum kernel estimation are added to a linear quantum support vector machine to construct a decision tree classifier using decision directed acyclic networks of quantum support vector machine nodes (QKE). The experimental findings demonstrate the effectiveness and adaptability of the suggested quantum classification technique. In terms of classification accuracy, speed, and practical application impact, the proposed classification approach outperforms the competition, with an accuracy difference from conventional classification algorithms being less than 1%. With improved accuracy and reduced expense as the incremental data increases, the efficiency of the suggested algorithm for incremental data classification is comparable to previous quantum classification algorithms. The proposed global decision tree paradigm addresses the critical issues that need to be resolved by quantum classification methods, such as the inability to process incremental data and the failure to take the cost of categorization into account. By integrating the Bayesian algorithm and the quantum decision tree classification algorithm and using QKE, the proposed method achieves high accuracy and efficiency while maintaining high performance when processing incremental sequences and considering classification costs. Overall, the theoretical and experimental findings demonstrate the effectiveness of the suggested quantum classification technique, which offers a promising solution for handling big data classification tasks that require high accuracy and efficiency.