Quantum algorithms have demonstrated extraordinary potential across numerous fields, offering significant advantages in solving practical problems. Power Quality Disturbances (PQDs) have always been a critical factor affecting the stability and safety of electrical power systems, and accurately detecting and identifying PQDs is crucial for ensuring reliable system operation. This paper explores the application of quantum algorithms in the field of power quality and proposes a novel method using Quantum Support Vector Machines (QSVM) to detect and identify PQDs, which marks the first application of QSVM in PQD analysis. The QSVM model employed involves three main stages: quantum feature mapping, quantum kernel computation, and model training. Quantum feature mapping uses quantum circuits to map classical data into a high-dimensional Hilbert space, enhancing feature separability. Quantum kernel computation calculates the inner products between features for model training. Rigorous theoretical and experimental analyses validate our approach. This method achieves a time complexity of $O(N^{2} \log (N))$ , superior to classical SVM algorithms. Simulation results show high accuracy in PQDs detection, achieving a 100% detection rate and a 96.25% accuracy rate in single PQD identification. Experimental outcomes demonstrate robustness, maintaining over 87% accuracy even with increased noise levels, confirming its effectiveness in PQDs detection and identification.
Quantum algorithms have demonstrated extraordinary potential across numerous fields, offering significant advantages in solving practical problems. Power Quality Disturbances (PQDs) have always been a critical factor affecting the stability and safety of electrical power systems, and accurately detecting and identifying PQDs is crucial for ensuring reliable system operation. This paper explores the application of quantum algorithms in the field of power quality and proposes a novel method using Quantum Support Vector Machines (QSVM) to detect and identify PQDs, which marks the first application of QSVM in PQD analysis. The QSVM model employed involves three main stages: quantum feature mapping, quantum kernel computation, and model training. Quantum feature mapping uses quantum circuits to map classical data into a high-dimensional Hilbert space, enhancing feature separability. Quantum kernel computation calculates the inner products between features for model training. Rigorous theoretical and experimental analyses validate our approach. This method achieves a time complexity of O(N2log(N)) , superior to classical SVM algorithms. Simulation results show high accuracy in PQDs detection, achieving a 100% detection rate and a 96.25% accuracy rate in single PQD identification. Experimental outcomes demonstrate robustness, maintaining over 87% accuracy even with increased noise levels, confirming its effectiveness in PQDs detection and identification.
Abstract As an emerging technology force, quantum algorithms have shown great potential and unique advantages in many fields of application. Power quality disturbances (PQDs) affect the security and stability of the power system, which may lead to equipment damage, system failures and economic losses. The accurate detection and recognition of PQDs is a guarantee for the safe operation of the system. In order to explore the application of quantum algorithms in the field of power quality, an improved quantum neural network(QNN) is proposed for PQDs detection and recognition. The QNN model consists of three parts: quantum encoding, quantum variational, and quantum measurement. Firstly, the classical data is mapped to the high-dimensional quantum feature space through the encoding layer, so that it is transformed into quantum data. Subsequently, a unique quantum circuit is constructed in the quantum variational layer to adjust the rotation angles and entanglement of data and ancilla qubits to realise quantum information transformation. The quantum information obtains the expected value through the measurement layer, which is used for model optimization and subsequent disturbance classification. The experimental results show that the detection accuracy of PQDs by this method reaches 99.75%, the recognition accuracy of single disturbance and mixed disturbance reaches 97.85% and 95.50%, respectively. The model uses fewer parameters to obtain high accuracy under the same conditions compared to classical machine learning models. In addition, comparative tests in different noise environments show the robustness of the model.
This study addresses the urgent need for improved prostate cancer detection methods by harnessing the power of advanced technological solutions. We introduce the application of Quantum Support Vector Machine (QSVM) to this critical healthcare challenge, showcasing an enhancement in diagnostic performance over the classical Support Vector Machine (SVM) approach. Our study not only outlines the remarkable improvements in diagnostic performance made by QSVM over the classic SVM technique, but it delves into the advancements brought about by the quantum feature map architecture, which has been carefully identified and evaluated, ensuring it aligns seamlessly with the unique characteristics of our prostate cancer dataset. This architecture succeded in creating a distinct feature space, enabling the detection of complex, non-linear patterns in the data. The findings reveal not only a comparable accuracy with classical SVM (92%) but also a 7.14% increase in sensitivity and a notably high F1-Score (93.33%). This study's important combination of quantum computing in medical diagnostics marks a pivotal step forward in cancer detection, offering promising implications for the future of healthcare technology.
Abstract This study addresses the urgent need for improved prostate cancer detection methods by harnessing the power of advanced technological solutions. We introduce the application of Quantum Support Vector Machine (QSVM) to this critical healthcare challenge, showcasing an enhancement in diagnostic performance over the classical Support Vector Machine (SVM) approach. Our study not only outlines the remarkable improvements in diagnostic performance made by QSVM over the classic SVM technique, but it delves into the advancements brought about by the quantum feature map architecture, which has been carefully identified and evaluated, ensuring it aligns seamlessly with the unique characteristics of our prostate cancer dataset. This architecture succeded in creating a distinct feature space, enabling the detection of complex, non-linear patterns in the data. The findings reveal not only a comparable accuracy with classical SVM (92%) but also a 7.14% increase in sensitivity and a notably high F1-Score (93.33%). This study's important combination of quantum computing in medical diagnostics marks a pivotal step forward in cancer detection, offering promising implications for the future of healthcare technology.
Cyberbullying has become much more common due to the proliferation of social media, which has devastating effects on users' mental health and relationships. Automatic identification of abusive textual content has therefore become an important research problem. Cyberbullying detection has seen extensive use of classical machine learning approaches, notably Support Vector Machines (SVMs); however, their effectiveness can be limited when handling high-dimensional and context-rich textual features. Recent developments in quantum computing introduce new computational paradigms that may enable more expressive feature representations through quantum-enhanced learning models. In this study, we explore the potential of a hybrid. Nowadays classical-quantum architecture to apply a Quantum Support Vector Machine (QSVM) to detect cyberbullying. Experiments were performed on a publicly available Kaggle dataset containing 35,824 labeled tweets. Before TF-IDF feature extraction, the text data underwent preprocessing via cleaning and tokenization. Dimension reduction was carried out using Principal Component Analysis (PCA) in respect of qubit constraints. Then, they were embedded into quantum states using angle embedding. The accuracy, precision, recall, and F1-score measures were used to compare QSVM models with various qubit configurations (2, 4, and 6), as well as a traditional SVM baseline, under an 80-20 stratified train-test split. The findings indicate that the quality of QSVM performance is highest with the increase in qubit numbers and the maximum accuracy of the quantum computing method is 60 percent according to six qubits, whereas the classical SVM achieves 67.5 percent. Although the quantum model currently incurs higher computational overhead due to simulator-based implementation, increasing qubit dimensionality enhances its ability to capture complex and non-linear linguistic patterns. These observations indicate that QSVM-based cyberbullying detection remains exploratory at present, with scope for further improvement as quantum hardware and algorithms advance.
Early detection of Autism Spectrum Disorder (ASD) is crucial for initiating interventions that can significantly improve developmental outcomes. Traditional diagnostic methods are often subjective and time-consuming, leading to delays in diagnosis. While classical machine learning models have been applied to ASD detection with promising results, they face challenges in processing complex, high-dimensional medical data. Quantum computing utilizes superposition, and entanglement offers a novel approach to handle such data more efficiently. This study investigates the application of Quantum Support Vector Machines (QSVM) for ASD detection using the Autism Screening Data for Toddlers (ASDTests) dataset. After preprocessing and feature selection, we trained a QSVM model. We compared its performance with classical machine learning models, including Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), and Naive Bayes (NB). We evaluated the models based on accuracy, precision, recall, and F1-score across multiple random subsets to assess robustness. The QSVM model achieved the highest mean accuracy (96.7 %), precision (97.2 %), and F1-score (95.9 %) with low variance, indicating strong and consistent performance. Statistical analyses using the Paired t-test and Friedman Test showed marginal improvements over the SVM model (p = 0.0495), with no significant difference compared to RF and NB models. These results suggest that QSVM can enhance the accuracy and robustness of ASD detection compared to classical models. Future work could explore hybrid models combining quantum and classical machine learning approaches to improve performance. Applying these methods to different ASD datasets may provide broader validation and insights into their generalizability.
With the increasing complexity of projects and the volume of data in Software Engineering (SE), the need for efficient and accurate data analysis techniques has become crucial. Classification algorithms play a vital role in various SE tasks, such as bug detection, software quality prediction, and requirements classification. Quantum computing offers a new paradigm with the potential to overcome classical computational limitations for certain types of problems. This research proposes the design and implementation of a kernel-based quantum classification algorithm (also known as Quantum Support Vector Machine - QSVM) tailored for data analysis in the SE domain. We will discuss the fundamental principles behind quantum feature mapping and quantum kernel matrices, and demonstrate its implementation using quantum computing libraries. As a case study, the designed algorithm will be tested on a software bug detection dataset, comparing its performance with classical kernel-based classification algorithms like Support Vector Machine (SVM). The result of the comparison show that QSVM is superior in terms of accuracy, precision, recall, and F1-score compared to SVM.