Classical Machine Learning (CML) involves training software models to make predictions or generative content utilizing datasets. In contrast, quantum computing involves the utilization of computers that consist of quantum bits which process more information than standard computers. Due to their performance, they can produce enhanced solutions to complex problems. Quantum Machine Learning (QML) combines the power of quantum computers alongside CML to address problems more efficiently and effectively than CML. This study will be examining various datasets from cybersecurity and engineering fields, where having quicker and more efficient solutions can lead to improved accuracy and performance. This was conducted by implementing the datasets into different models of QML and comparing the results to CML. The models of QML include the Quantum Neural Network model, the Quantum Random Forest Classifier model, and the Quantum Support Vector Machine Learning model. This study aims to evaluate the effectiveness of the QML in providing more accurate and faster solutions compared to the traditional CML method.
Classical Machine Learning (CML) involves training software models to make predictions or generative content utilizing datasets. In contrast, quantum computing involves the utilization of computers that consist of quantum bits which process more information than standard computers. Due to their performance, they can produce enhanced solutions to complex problems. Quantum Machine Learning (QML) combines the power of quantum computers alongside CML to address problems more efficiently and effectively than CML. This study will be examining various datasets from cybersecurity and engineering fields, where having quicker and more efficient solutions can lead to improved accuracy and performance. This was conducted by implementing the datasets into different models of QML and comparing the results to CML. The models of QML include the Quantum Neural Network model, the Quantum Random Forest Classifier model, and the Quantum Support Vector Machine Learning model. This study aims to evaluate the effectiveness of the QML in providing more accurate and faster solutions compared to the traditional CML method.
This study investigates the synergy between Classical Machine Learning (CML) and Quantum Machine Learning (QML) in analyzing security datasets, conducting a comparative analysis using models based on QML and CML to evaluate their performance as data sizes and iteration counts increase.The author, specifically, employs popular machine learning methods, including Support Vector Machines (SVM), Neural Networks (NN), and Logistic Regression (LR), to assess these techniques on real-world security datasets, such as network intrusion detection data and malware classification logs.The primary focus is determining the effectiveness and efficiency of QML and CML approaches in handling large-scale security data.Through rigorous experimentation, the study highlights the benefits and drawbacks of both QML and CML, indicating that while QML offers significant speedups in processing times for large datasets due to quantum parallelism, it faces challenges in terms of hardware accessibility and noise sensitivity, while CML methods, though slower with massive data, benefit from mature algorithms and more robust infrastructure.The outcomes provide critical insights into the practicality of applying QML and CML to security-related applications, demonstrating that QML techniques can outperform CML in specific scenarios, such as real-time threat detection, due to their superior computational efficiency.However, the current limitations of quantum hardware suggest that CML remains more practical for many applications in the short term.This work significantly advances the state of the art in Quantum Machine Learning.It offers vital guidance for practitioners and researchers in security data analysis, underscoring the potential of QML to revolutionize security data processing while acknowledging the ongoing need for advancements in quantum computing technology.
In cybersecurity, protecting sensitive data from malicious attacks is of paramount importance. Classical machine learning algorithms have shown limitations in this domain due to their inability to handle complex, highly dynamic, and adversarial attack scenarios. Quantum machine learning (QML) algorithms, with their enhanced processing capacity and robustness against such attacks, offer a promising alternative to address these shortcomings. Consequently, QML can significantly improve the detection and mitigation of sophisticated and evolving threats. This study explores the application of QML algorithms, specifically the quantum support vector machine (QSVM) and a hybrid quantum neural network (QNN) and QSVM architecture (QSVM-QNN), on two real-world cybersecurity datasets: APA-DDoS and CIC-IoT2022. Initially, different qubit counts and encoding techniques are investigated to assess the effectiveness of the swap test and UU† methods for inner product calculation, which is later employed in the QSVM algorithm. Subsequently, the performance of the proposed QSVM and QSVM-QNN models is evaluated and compared against classical counterparts across varying numbers of qubits and encoding schemes. The results show that the proposed QSVM model achieved accuracies of 1.000 and 0.985, while the QSVM-QNN model reached accuracies of 0.970 and 1.000, respectively, on the two datasets, demonstrating strong performance and surpassing several classical models. Furthermore, to assess robustness, the proposed algorithms were tested under various noise models, highlighting their capability to provide effective solutions for cybersecurity challenges in realistic quantum noisy environments.
In this paper, we apply Quantum Machine Learning to analyze security datasets. We compare cross-models, Quantum Machine Learning (QML) against Classical Machine Learning (CML), performance with increasing data size, and performance with increasing iteration numbers using commonly used machine learning techniques such as Neural Networks (NN), Support Vector Machines (SVM), and Logistic Regression (LR). Our study focuses on assessing the accuracy of QML and CML approaches on real-world security datasets. The results provide light on the advantages and disadvantages of both QML and CML methodologies, with implications for their use in security data analysis. The experimental findings provide useful information on the applicability of QML and CML for security-related applications. The study contributes to the growing field of quantum machine learning research, particularly in the context of security data analysis, and offers helpful advice for academics and practitioners working in this area.
In this work, we studied the use of Quantum Machine Learning (QML) algorithms for binary classification and compared their performance with classical Machine Learning (ML) methods. QML merges principles of Quantum Computing (QC) and ML, offering improved efficiency and potential quantum advantage in data-driven tasks and when solving complex problems. In binary classification, where the goal is to assign data to one of two categories, QML uses quantum algorithms to process large datasets efficiently. Quantum algorithms like Quantum Support Vector Machines (QSVM) and Quantum Neural Networks (QNN) exploit quantum parallelism and entanglement to enhance performance over classical methods. This study focuses on two common QML algorithms, Quantum Support Vector Classifier (QSVC) and QNN. We used the Qiskit software and conducted the experiments with three different datasets. Data preprocessing included dimensionality reduction using Principal Component Analysis (PCA) and standardization using scalers. The results showed that quantum algorithms demonstrated competitive performance against their classical counterparts in terms of accuracy, while QSVC performed better than QNN. These findings suggest that QML holds potential for improving computational efficiency in binary classification tasks. This opens the way for more efficient and scalable solutions in complex classification challenges and shows the complementary role of quantum computing.
Abstract - The exponential growth of large-scale data has created significant challenges in storage, processing, and optimization that classical computing systems struggle to address efficiently. Quantum Machine Learning (QML) has emerged as a promising paradigm that integrates quantum computing concepts with machine learning techniques to tackle complex big data optimization problems. By utilizing quantum features such as superposition, entanglement, and parallelism, QML algorithms can accelerate tasks like clustering, classification, and dimensionality reduction, which are traditionally computationally demanding. Recent advancements indicate strong potential for quantum-inspired optimization in fields including cybersecurity, financial portfolio management, Industry 4.0, and healthcare analytics. This paper provides a comprehensive survey of QML algorithms designed for big data optimization, examining their advantages, limitations, and application domains. Additionally, it discusses the performance trade-offs associated with hybrid quantum–classical frameworks and highlights future research directions aimed at creating secure, scalable, and resource-efficient quantum solutions for large-scale, data-driven decision-making. As data volumes continue to expand at extremely rapid rates, processing big data with traditional machine learning (ML) techniques has become increasingly challenging. ML models often struggle with issues related to computational power, efficient parameter tuning, accurate model selection, and maintaining high performance when dealing with massive datasets. Deep learning approaches—such as convolutional neural networks (CNNs)—demand substantial computational resources to train on large-scale supervised learning tasks. Moreover, the difficulty of training these networks grows significantly as the size and complexity of the datasets increase. In response to these limitations, Quantum Machine Learning (QML) has emerged as a promising research domain at the intersection of quantum computing and machine learning. Quantum computers operate fundamentally differently from classical systems, using the principles of quantum mechanics to encode and process information. As a result, quantum computational methods have the potential to address certain problems that are computationally intensive or infeasible for classical machines. Index Terms— Quantum Machine Learning (QML); Big Data Optimization; Quantum Algorithms; Hybrid Quantum-Classical Models; Quantum Computing; Data Analytics
With the potential for quantum computing to completely transform cybersecurity, quantum machine learning is becoming a ground-breaking technology. Cyber-attacks have been successfully countered by traditional network defense systems, which mostly use conventional machine learning (ML) techniques. However, the growing complexity of assaults and the exponential expansion of network data reveal the shortcomings of traditional methods, especially with regard to speed and scalability. By utilizing quantum algorithms like Quantum Support Vector Machines (QSVM), Quantum Neural Networks (QNN), and Variational Quantum Circuits (VQC), Quantum Machine Learning (QML) presents a viable substitute. These methods are especially well-suited for real-time network defense scenarios since they show the capacity to handle high-dimensional data, identify complex patterns, and increase computational efficiency. In this research, the effectiveness of QML techniques in network defense is thoroughly examined and contrasted with that of traditional machine learning techniques. The study emphasizes how QML may surpass conventional models in terms of accuracy, scalability, and resilience against sophisticated threats by concentrating on use cases like intrusion detection, malware analysis, and anomaly detection. The necessity for hybrid quantum-classical models and the present constraints of quantum hardware are among the difficulties that are discussed in the study. This study highlights the revolutionary potential of QML in strengthening network defense mechanisms and identifies crucial areas for further research, paving the road for a secure digital future by combining theoretical ideas with experimental discoveries.