The thesis explores the development of the Quantum Pattern Extraction and Classification (QPEC) algorithm, a hybrid quantum-classical method designed to classify devices based on their PWM signals. In cybersecurity, the QPEC algorithm can identify PWM signal distortion for device authentication and in fault detection by recognizing subtle signal anomalies that indicate hardware degradation. The research addresses the challenge of accurately identifying subtle differences in PWM signals, which classical methods struggle to handle effectively. The Primary research question investigates whether quantum computing can enhance feature extraction and improve classification accuracy compared to traditional approaches. To achieve this, the QPEC algorithm utilizes a parameterized quantum circuit (PQC) with entanglement layers to map PWM signal features into a high-dimensional quantum space, capturing complex correlations. Classical optimization techniques, including stochastic gradient descent, are applied to refine the quantum circuit parameters, and machine learning models are integrated for classification. The methodology combines quantum simulations, training, and testing on real-world PWM datasets, with performance evaluated using metrics such as accuracy, precision, recall and F1-score. This hybrid quantum-classical approach has a 88.7% accuracy compared to classic state vector machines of 85.3% and random forest classifier of 86.1%.
The thesis explores the development of the Quantum Pattern Extraction and Classification (QPEC) algorithm, a hybrid quantum-classical method designed to classify devices based on their PWM signals. In cybersecurity, the QPEC algorithm can identify PWM signal distortion for device authentication and in fault detection by recognizing subtle signal anomalies that indicate hardware degradation. The research addresses the challenge of accurately identifying subtle differences in PWM signals, which classical methods struggle to handle effectively. The Primary research question investigates whether quantum computing can enhance feature extraction and improve classification accuracy compared to traditional approaches. To achieve this, the QPEC algorithm utilizes a parameterized quantum circuit (PQC) with entanglement layers to map PWM signal features into a high-dimensional quantum space, capturing complex correlations. Classical optimization techniques, including stochastic gradient descent, are applied to refine the quantum circuit parameters, and machine learning models are integrated for classification. The methodology combines quantum simulations, training, and testing on real-world PWM datasets, with performance evaluated using metrics such as accuracy, precision, recall and F1-score. This hybrid quantum-classical approach has a 88.7% accuracy compared to classic state vector machines of 85.3% and random forest classifier of 86.1%.
The limitations of Global Positioning Systems (GPS) in indoor environments have led to the development of a number of indoor localization systems. One potential system is based on WiFi signals. Wifi-based Localization systems leverage Wireless Local Area Network (WLAN) fingerprinting to estimate user positions.This thesis explores a hybrid quantum-classical machine learning approach to improve wifi-based indoor localization systems, where quantum circuits extract features while classical models handle classification. The proposed quantum-classical hybrid system achieved 96.83% accuracy in noise-free conditions. The results are further compared with classical machine learning algorithms, including K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Random Forest (RF), to benchmark our hybrid model. Our model outperformed all these models, where KNN, SVM, and Random Forest achieved accuracy rates of 74.2%, 92.0%, and 95.4%, respectively. We also included other popular metrics and obtained promising results, where our model achieved precision of 97%, recall of 96.5%, and F1 score of 96.8%, outperforming all the classical models we benchmarked with. However, the hybrid model outperformed them. However, real-world quantum systems face noise-related challenges, such as depolarizing errors, bit-flip errors, and decoherence, which degrade performance. Therefore, we adopted a number of popular noise-models and evaluated our proposed model accordingly. Our model showed resistance to noise, especially in low to moderate noise rates.The research demonstrates how quantum computing can surpass machine learning, opening new avenues for indoor navigation, human motion detection, and intrusion detection.
This study explores the application of quantum machine learning (QML) algorithms to enhance cybersecurity threat detection, particularly in the classification of malware and intrusion detection within high-dimensional datasets. Classical machine learning approaches encounter limitations when dealing with intricate, obfuscated malware patterns and extensive network intrusion data. To address these challenges, we implement and evaluate various QML algorithms, including Quantum Neural Networks (QNN), Quantum Support Vector Machines (QSVM), and hybrid Quantum Convolutional Neural Networks (QCNN) for malware detection tasks. Our experimental analysis utilized two datasets: the Intrusion dataset, comprising 150 samples with 56 memory-based features derived from Volatility framework analysis, and the ObfuscatedMalMem2022 dataset, containing 58,596 samples with 57 features representing benign and malicious software. Remarkably, our QML methods demonstrated superior performance compared to classical approaches, achieving accuracies of 95% for QNN and 94% for QSVM. These quantum-enhanced methods leveraged quantum superposition and entanglement principles to accurately identify complex patterns within highly obfuscated malware samples that were imperceptible to classical methods. To further advance malware analysis, we propose a novel real-time malware analysis framework that incorporates Quantum Feature Extraction using Quantum Fourier Transform, Quantum Feature Maps, and Classification using Variational Quantum Circuits. This system integrates explainable AI methods, including GradCAM++ and ScoreCAM algorithms, to provide interpretable insights into the quantum decision-making processes.
This research explores quantum machine learning for image classification, comparing classical and quantum models. A classical convolutional neural network (CNN), based on the LeNet architecture, achieved 99.12% accuracy on MNIST, outperforming a quantum CNN, which attained 93.30%. Quantum encoders applied to Fashion-MNIST reached 85.60%, while quantum support vector machine (SVM), implemented for binary classification, obtained 71.34%. The study integrates hybrid quantum-classical models, leveraging quantum circuits for feature extraction. A quantum autoencoder-based classifier was introduced, demonstrating efficient data compression and classification without additional qubits. Feature encoding using RX gates mapped classical data into quantum states, and the impact of parameterized quantum circuit structures on classification accuracy was analyzed. Results indicate that specific ansatz structures yield superior accuracy, highlighting the significance of quantum circuit design. While classical models currently outperform quantum counterparts, quantum approaches reduce trainable parameters and computational complexity. Limitations in current quantum hardware, including noise and scalability, restrict performance. However, advancements in error mitigation, quantum gate efficiency, and scalable quantum architecture could bridge the gap with classical methods. These quantum techniques hold potential in aerospace applications, such as satellite image classification, anomaly detection in remote sensing, and real-time processing of high-dimensional image data. This study underscores the growing role of quantum circuits in machine learning, particularly in feature extraction and classification, suggesting that future quantum hardware could unlock new capabilities in end-to-end learning beyond hybrid approaches.
This research explores the integration of quantum computing with classical machine learning to enhance data classification tasks using Quantum Neural Networks (QNN) and Parameterized Quantum Circuits (PQC). The hybrid approach leverages the advantages of both quantum and classical systems to improve the efficiency and accuracy of data processing. In this model, data is encoded into qubits using amplitude encoding, representing input vectors as amplitudes of quantum states. The QNN is initialized by placing the qubits in superposition using Hadamard gates, followed by data encoding with parameterized rotational gates that map classical data to quantum states using rotation angles. PQC plays a central role by applying layers of parameterized quantum operations to process data in the quantum space. These parameters are optimized during the training process, where a quadratic loss function minimizes the error between the predicted quantum states and the true class labels using gradient descent. Experiments conducted on the MNIST dataset show that the hybrid the hybrid quantum-classical neural network (QCNN) with PQC achieves a classification accuracy of over 95%, highlighting its potential in machine learning applications. The results demonstrate that integrating quantum computing with classical machine learning enhances performance in complex data analysis tasks due to the exponential growth of quantum state space and the parallelism of quantum systems, making hybrid models promising for computer vision and classification tasks.
This study investigates the effectiveness of a hybrid quantum-classical framework for plant disease detection, with a focus on improving classification accuracy compared to conventional approaches. Image-based datasets of crop diseases were processed and encoded into variational quantum circuits (VQCs), which were then integrated with classical machine learning classifiers. The proposed model achieved an overall classification accuracy of 95.2%, outperforming a baseline convolutional neural network that recorded 91.4% accuracy on the same dataset. Performance evaluation was carried out using multiple metrics including precision, recall, and F1-score, with the hybrid approach showing consistent gains across all categories. Furthermore, the model demonstrated reduced overfitting and improved robustness when tested under noisy and high-dimensional input conditions. These results highlight the potential of quantum-assisted machine learning to significantly enhance crop disease detection, offering a scalable and accurate pathway for sustainable agriculture and precision farming.
Quantum machine learning has emerged as a promising frontier, leveraging the principles of quantum computing to enhance classical algorithms. This paper proposes an optimized Quantum Support Vector Classifier (QSVC) for the classification of the MNIST dataset (a benchmark in handwritten digit recognition). We employ a Chebyshev Parameterized Quantum Circuit (PQC) with an increased number of qubits and layers, and utilize an existing projected quantum kernel method to effectively capture the complex patterns in the data. To enhance model robustness, data augmentation techniques are applied, and dimensionality reduction is performed using Principal Component Analysis (PCA). The proposed QSVC demonstrates competitive accuracy on both the training and testing datasets, highlighting the potential of quantum kernel methods in practical machine learning applications. A comparative analysis with classical Support Vector Machines (SVM) demonstrates the feasibility of quantum methods for high-dimensional classification tasks. Additionally, we investigate the impact of varying the number of qubits in the quantum circuit, providing in-depth insights into the scalability and performance trade-offs of quantum kernel methods.