Recent investigations into Quantum Machine Learning (QML) techniques have unveiled methodologies that accelerate training in established machine learning models to provide an alternative for capturing complex patterns. This study focuses on implementing a practical QML Algorithm, Variational Quantum Classification (VQC) for cybersecurity dataset so that detecting anomalies can be improved and faster by reducing number of attributes $\log_{2} M$ while training the model using Qiskit. Also, we study quantum algorithms to understand how it impacts on cyber datasets to detect anomalies in a improved way as it follows logarithms in the dimensionality reduction of quantum states which opens new horizons to quantum big data applications. Most importantly, we aim to also investigate the impact of various parameterized quantum circuits on VQC using quantum data as quantum states encoded by the cyber security dataset, NSL-KDD. In this research, we train VQC with various structures and parameters of quantum circuits as well as optimizers to adjust parameters of quantum circuits (ansatz) to minimize the objective function values so as to improve accuracy of the model in which quantum circuit, EfficientSU2, along with optimizer, COBYLA, outperforms the accuracy than other circuits and optimizers which shows great potential for improving cybersecurity systems. The research could effectively bridge in the gap between theory and implementation based quantum machine learning on cybersecurity systems.Recent investigations into Quantum Machine Learning (QML) techniques have unveiled methodologies that accelerate training in established machine learning models to provide an alternative for capturing complex patterns. This study focuses on implementing a practical QML Algorithm, Variational Quantum Classification (VQC) for cybersecurity dataset so that detecting anomalies can be improved and faster by reducing number of attributes$\log_{2} M$while training the model using Qiskit. Also, we study quantum algorithms to understand how it impacts on cyber datasets to detect anomalies in a improved way as it follows logarithms in the dimensionality reduction of quantum states which opens new horizons to quantum big data applications. Most importantly, we aim to also investigate the impact of various parameterized quantum circuits on VQC using quantum data as quantum states encoded by the cyber security dataset, NSL-KDD. In this research, we train VQC with various structures and parameters of quantum circuits as well as optimizers to adjust parameters of quantum circuits (ansatz) to minimize the objective function values so as to improve accuracy of the model in which quantum circuit, EfficientSU2, along with optimizer, COBYLA, outperforms the accuracy than other circuits and optimizers which shows great potential for improving cybersecurity systems. The research could effectively bridge in the gap between theory and implementation based quantum machine learning on cybersecurity systems.

IEEE Annual International Computer Software and Applications Conference (COMPSAC)Jul 2, 202418 citations

Enhancing cyber security using quantum computing and Artificial Intelligence: A review

Abdullah Hill Hussain, Md Masud Hasan, Nayem Uddin Prince +3 more

The rapid development of cyber threats calls for solutions to bolster cyber security frameworks. This article reviews how Quantum Computing (QC) and AI are already beginning to address the criticisms cyber security systems typically take. Classical computing, which processes data sequentially using algorithms that are based on deterministic logic, confronts constraints from both scales and is efficient when dealing with massive datasets, resulting in slower threat detection times and a higher frequency of false positives. What's different is that quantum computing uses quantum mechanics to make data processing faster and more accurate. Using techniques like superposition and quantum-enhanced algorithms can deliver more precise threat analysis in a shorter period than can be achieved with classical methods. Quantum Machine Learning (QML) techniques such as quantum support vector machines (SVM) and variational quantum circuits handle big datasets much more efficiently than classical methods, so they enable better threat detection. Entrepreneurs also benefit from AI methods based on artificial intelligence via learning mechanisms that can automatically detect anomalies in a synergistic relationship with quantum developments to improve the accuracy of threat classification and response. With the combination of quantum algorithms and AI, cybersecurity is expected to see significantly enhanced speed, accuracy, and scalability, especially in large-scale deployment scenarios. However, problems still exist in the implementation of quantum technology - such as the need for compatible hardware and developing cryptographic methods that are in quantum times ahead. This paper points out how QC and AI could reinvent cyber security and provides an agenda that future research must take up in these areas.The rapid development of cyber threats calls for solutions to bolster cyber security frameworks. This article reviews how Quantum Computing (QC) and AI are already beginning to address the criticisms cyber security systems typically take. Classical computing, which processes data sequentially using algorithms that are based on deterministic logic, confronts constraints from both scales and is efficient when dealing with massive datasets, resulting in slower threat detection times and a higher frequency of false positives. What's different is that quantum computing uses quantum mechanics to make data processing faster and more accurate. Using techniques like superposition and quantum-enhanced algorithms can deliver more precise threat analysis in a shorter period than can be achieved with classical methods. Quantum Machine Learning (QML) techniques such as quantum support vector machines (SVM) and variational quantum circuits handle big datasets much more efficiently than classical methods, so they enable better threat detection. Entrepreneurs also benefit from AI methods based on artificial intelligence via learning mechanisms that can automatically detect anomalies in a synergistic relationship with quantum developments to improve the accuracy of threat classification and response. With the combination of quantum algorithms and AI, cybersecurity is expected to see significantly enhanced speed, accuracy, and scalability, especially in large-scale deployment scenarios. However, problems still exist in the implementation of quantum technology - such as the need for compatible hardware and developing cryptographic methods that are in quantum times ahead. This paper points out how QC and AI could reinvent cyber security and provides an agenda that future research must take up in these areas.

World Journal of Advanced Research and ReviewsJun 30, 20218 citations

Quantum Machine Learning Framework for Advanced Cyber Security Threat Detection and Analysis

J. David Sukeerthi Kumar, Bachala Ganesh, M. V. Subramanyam +3 more

International Conference on Intelligent and Sustainable AI Systems (ICOSAAS)Jul 1, 20260 citations

Quantum Machine Learning: Perspectives in Cybersecurity

Davide Pastorello

Lecture notes in computer scienceJan 1, 20241 citation

Unlocking Big Data with Quantum Machine Learning

Dr.Farheen Mohammed

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 AnalyticsAbstract - 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

INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENTDec 5, 20250 citations

\log_{2} M

while training the model using Qiskit. Also, we study quantum algorithms to understand how it impacts on cyber datasets to detect anomalies in a improved way as it follows logarithms in the dimensionality reduction of quantum states which opens new horizons to quantum big data applications. Most importantly, we aim to also investigate the impact of various parameterized quantum circuits on VQC using quantum data as quantum states encoded by the cyber security dataset, NSL-KDD. In this research, we train VQC with various structures and parameters of quantum circuits as well as optimizers to adjust parameters of quantum circuits (ansatz) to minimize the objective function values so as to improve accuracy of the model in which quantum circuit, EfficientSU2, along with optimizer, COBYLA, outperforms the accuracy than other circuits and optimizers which shows great potential for improving cybersecurity systems. The research could effectively bridge in the gap between theory and implementation based quantum machine learning on cybersecurity systems.