Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Applied Artificial Intelligence

PegasosQSVM: A Quantum Machine Learning Approach for Accurate Fake News Detection

Mehdi Khalil, Chi Zhang, Zhiwei Ye, Peng Zhang

Published Jan 27, 2025• 7 citations
Publisher

Abstract

The rapid spread of fake news on social media poses a significant threat to modern societies. Traditional machine learning approaches have limitations in handling the ever-increasing volume and complexity of data. This research explores quantum machine learning for fake news classification by proposing Pegasos Quantum Support Vector Machines, a novel algorithm combining Pegasos Support Vector Machines with quantum kernels, and advanced data encoding. Through experimentation on the IBM Qasm Simulator, Pegasos Quantum Support Vector Machines scored 90.67% in accuracy. This study is primarily focused on local simulation, where the proposed algorithm scored as high as 95.63%, with 95.44% precision, 99.52% recall, and 96.76% f1-score. The achieved results outperform other machine learning methods on the BUZZFEED dataset, including Quantum Neural Networks and Quantum K-Nearest Neighbors. Its successful implementation paves the way for further refinement of quantum machine learning techniques in fake news classification. The PegasosQSVM algorithm encounters, however, some implementation issues on real world Quantum Processing Units(QPU). Noisy Intermediate-Scale Quantum era QPU are prone to noise effects that affect the computations negatively, and by extension, the results of quantum machine learning algorithms. Further implementation on real QPU and use of error mitigation techniques, are needed for optimal results on quantum hardware.

DOI
10.1080/08839514.2025.2457207

Related papers

QCNN-MFND: A Novel Quantum CNN Framework for Multimodal Fake News Detection in Social Media

Arya Suneesh, Balasubramanian Palani

Fake news on social media platforms poses significant threats to public trust and information integrity.This research explores the application of quantum machine learning (QML) techniques for detecting fake news by leveraging quantum computing's unique capabilities.Our work introduces a hybrid quantum-classical framework that utilizes quantum convolutional neural networks (QCNNs) with angle and amplitude encoding schemes for processing multimodal features from text and images.Experiments conducted on benchmark datasets -Gos-sipCop and Politifact -demonstrate that our quantum-enhanced model achieves superior performance compared to classical approaches, with accuracy rates of 88.52% and 85.58%, and F1 scores of 93.19% and 90.20% respectively.Our findings establish QML as a viable approach for addressing the challenges of fake news detection in the digital era.

•Jan 1, 2025•1 citation

Revolutionizing Fake News Detection: A Quantum Machine Learning Paradigm

Ainaz Jamshidi, Muhammad Saleem Arif

Today’s digital world facilitates the rapid growth of social media platforms and online news sources and consequently the spread of true and false information. While the detection of fake news is extensively studied using classical machine learning (ML)-based approaches, quantum ML approaches remain largely unexplored. In this study, we aim to investigate the effectiveness of a hybrid quantum-classical neural network on three open access datasets. This study proposes a Hybrid Quantum Neural Network (HQNN) model for classifying news articles as either "fake" or "real". The approach combines classical machine learning techniques with quantum computing to leverage the strengths of both paradigms. It incorporates a quantum layer, implemented via a parameterized quantum circuit that utilizes angle embedding and entanglement to extract complex features, followed by a classical fully connected layer for final decision-making. Our results demonstrate the effectiveness of the HQNN model, achieving high accuracy across multiple datasets, with values reaching up to 90.71%. These findings highlight the potential of hybrid quantum-classical models to improve fake news detection and pave the way for further research into quantum-enhanced ML applications.

Conference on Information Sciences and Systems•Mar 19, 2025•0 citations

Heart failure prediction using Pegasos Quantum Support Vector Classifier

Bagoun Yassine

Heart failure (HF) is a critical global health problem, and early diagnosis with accuracy is highly essential for effective treatment. the combination of machine learning (ML) with quantum physics has driven research into quantum algorithms for data processing, offering new methods in novel computational environments. While quantum computing is still in its infancy, with quantum algorithms being actively tested and developed, this study proposes a Quantum Machine Learning (QML) approach for HF prediction using the Pegasos Quantum Support Vector Classifier (QSVC) algorithm. Addressing a critical research gap—the lack of direct comparisons between this algorithm and classical ML algorithms in HF — this study conducts a comprehensive evaluation of Pegasos (Primal Estimated sub-GrAdient SOlver for SVM) QSVC against classical models, including Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and k-Nearest Neighbors (K-NN), using a real-world electronic health dataset. The proposed work utilizes quantum feature maps and kernels to improve HF prediction. the results showed that Pegasos QSVC leveraging quantum principles has potential in handling high dimensional data. The results highlight complementary strengths of the quantum and classical approaches. This work establishes the foundation for further QML research highlighting the technologies potential to transform healthcare predictive modeling.

Journal of Information Systems Engineering & Management•May 12, 2025•0 citations

A Scoping Survey of Quantum Machine Learning and Deep Learning for Real-World Applications

C Aishwarya, M. Venkatesan, P. Prabhavathy

Many prevalent issues in today’s society, such as fake news detection, can be efficiently addressed using artificial intelligence and machine learning techniques. The rapid dissemination of fake news through social media makes it challenging to verify the validity of information. QML and QDL represent promising frontiers for building models to address these challenges, with the prospect of achieving quantum advantage driving advancements in the field. This study examines and compares literature on QML and QDL models, including QSVM, VQE, QNN, QCNN, and RQNN, using the MNIST dataset. The effectiveness and scope of application of these models for fake news detection and other real-world applications are analyzed.

Procedia Computer Science•Jan 1, 2025•10 citations

Benchmarking quantum machine learning methods for intrusion detection on noisy quantum computers

Franco Cirillo, Christian Esposito, Jung Taek Seo

Intrusion detection systems (IDS) are essential for identifying cyber threats in complex digital environments. Machine learning (ML) is widely used to improve IDS by detecting anomalies, but classical ML methods often struggle with high-dimensional data and evolving threats. Quantum machine learning (QML) has been proposed as a potential paradigm to overcome some of these limitations, but is constrained by noisy intermediate-scale quantum (NISQ) challenges, affecting quality. This study systematically evaluates three QML models, Pegasos Quantum Support Vector Classifier (QSVC), Variational Quantum Classifier (VQC), and a Hybrid Quantum–Classical Neural Network (HQNN), for network anomaly detection. The models were optimized and tested on the ToN_IoT and NSL-KDD datasets using IBM quantum simulators under both ideal and noisy conditions. Performance was analyzed through the F1-score distribution as a function of circuit complexity, revealing how entanglement and noise affect robustness across different backends. Comparisons with classical models contextualize the current maturity of QML for cybersecurity, while computational time was used as an indicator of model complexity to explore accuracy–efficiency trade-offs. Among all configurations, Pegasos-QSVC achieved the best results, with 94.60% accuracy and an F1-score of 94.13%. The findings provide practical guidelines for designing noise-resilient QML models and highlight their potential for reliable intrusion detection under realistic quantum conditions.

Quantum Machine Intelligence•Mar 11, 2026•4 citations

Experimental Benchmarking of Quantum Machine Learning Classifiers

Mohamed Aly, Salma Fadaaq, Omnia Abu Waraga +2 more

Quantum Machine Learning (QML) has emerged as an interdisciplinary field that harnesses the potential of quantum computing to revolutionize classical machine learning paradigms. This paper presents a benchmarking study focused on four quantum classifier algorithms: Quantum Support Vector Classifier (QSVC), Variational Quantum Circuit (VQC), Quantum Neural Network Classifier (QNNC) and Pegasos Quantum Support Vector Classifier (PegasosQSVC). These quantum classifiers hold promise for unlocking quantum advantages in classification tasks. However, a systematic assessment of their performance across diverse datasets is yet to be fully explored. In this research study, we evaluate and compare the classification performance of quantum machine learning classifiers on various datasets (Breast Cancer, Diabetes, and Titanic) and compare them to a classical model. Our findings revealed that PegasosQSVC consistently maintained high performance across all datasets with the highest average accuracy, recall, and F1-score values of 0.81, 0.72, and 0.71, respectively, followed by VQC, followed by VQC, while QSVC and QNNC perform variably. Nevertheless, QSVC was found to balance performance and training time, achieving an average accuracy of 0.81 and a training time of 177 seconds. Overall, the classical model surpassed QML models as it was up to 10% higher in average accuracy.

International Conference on Signal Processing and Information Security (ICSPIS)•Nov 8, 2023•10 citations
The rapid spread of fake news on social media poses a significant threat to modern societies. Traditional machine learning approaches have limitations in handling the ever-increasing volume and complexity of data. This research explores quantum machine learning for fake news classification by proposing Pegasos Quantum Support Vector Machines, a novel algorithm combining Pegasos Support Vector Machines with quantum kernels, and advanced data encoding. Through experimentation on the IBM Qasm Simulator, Pegasos Quantum Support Vector Machines scored 90.67% in accuracy. This study is primarily focused on local simulation, where the proposed algorithm scored as high as 95.63%, with 95.44% precision, 99.52% recall, and 96.76% f1-score. The achieved results outperform other machine learning methods on the BUZZFEED dataset, including Quantum Neural Networks and Quantum K-Nearest Neighbors. Its successful implementation paves the way for further refinement of quantum machine learning techniques in fake news classification. The PegasosQSVM algorithm encounters, however, some implementation issues on real world Quantum Processing Units(QPU). Noisy Intermediate-Scale Quantum era QPU are prone to noise effects that affect the computations negatively, and by extension, the results of quantum machine learning algorithms. Further implementation on real QPU and use of error mitigation techniques, are needed for optimal results on quantum hardware.
Fake news on social media platforms poses significant threats to public trust and information integrity.This research explores the application of quantum machine learning (QML) techniques for detecting fake news by leveraging quantum computing's unique capabilities.Our work introduces a hybrid quantum-classical framework that utilizes quantum convolutional neural networks (QCNNs) with angle and amplitude encoding schemes for processing multimodal features from text and images.Experiments conducted on benchmark datasets -Gos-sipCop and Politifact -demonstrate that our quantum-enhanced model achieves superior performance compared to classical approaches, with accuracy rates of 88.52% and 85.58%, and F1 scores of 93.19% and 90.20% respectively.Our findings establish QML as a viable approach for addressing the challenges of fake news detection in the digital era.
Today’s digital world facilitates the rapid growth of social media platforms and online news sources and consequently the spread of true and false information. While the detection of fake news is extensively studied using classical machine learning (ML)-based approaches, quantum ML approaches remain largely unexplored. In this study, we aim to investigate the effectiveness of a hybrid quantum-classical neural network on three open access datasets. This study proposes a Hybrid Quantum Neural Network (HQNN) model for classifying news articles as either "fake" or "real". The approach combines classical machine learning techniques with quantum computing to leverage the strengths of both paradigms. It incorporates a quantum layer, implemented via a parameterized quantum circuit that utilizes angle embedding and entanglement to extract complex features, followed by a classical fully connected layer for final decision-making. Our results demonstrate the effectiveness of the HQNN model, achieving high accuracy across multiple datasets, with values reaching up to 90.71%. These findings highlight the potential of hybrid quantum-classical models to improve fake news detection and pave the way for further research into quantum-enhanced ML applications.
Heart failure (HF) is a critical global health problem, and early diagnosis with accuracy is highly essential for effective treatment. the combination of machine learning (ML) with quantum physics has driven research into quantum algorithms for data processing, offering new methods in novel computational environments. While quantum computing is still in its infancy, with quantum algorithms being actively tested and developed, this study proposes a Quantum Machine Learning (QML) approach for HF prediction using the Pegasos Quantum Support Vector Classifier (QSVC) algorithm. Addressing a critical research gap—the lack of direct comparisons between this algorithm and classical ML algorithms in HF — this study conducts a comprehensive evaluation of Pegasos (Primal Estimated sub-GrAdient SOlver for SVM) QSVC against classical models, including Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and k-Nearest Neighbors (K-NN), using a real-world electronic health dataset. The proposed work utilizes quantum feature maps and kernels to improve HF prediction. the results showed that Pegasos QSVC leveraging quantum principles has potential in handling high dimensional data. The results highlight complementary strengths of the quantum and classical approaches. This work establishes the foundation for further QML research highlighting the technologies potential to transform healthcare predictive modeling.
Many prevalent issues in today’s society, such as fake news detection, can be efficiently addressed using artificial intelligence and machine learning techniques. The rapid dissemination of fake news through social media makes it challenging to verify the validity of information. QML and QDL represent promising frontiers for building models to address these challenges, with the prospect of achieving quantum advantage driving advancements in the field. This study examines and compares literature on QML and QDL models, including QSVM, VQE, QNN, QCNN, and RQNN, using the MNIST dataset. The effectiveness and scope of application of these models for fake news detection and other real-world applications are analyzed.
Intrusion detection systems (IDS) are essential for identifying cyber threats in complex digital environments. Machine learning (ML) is widely used to improve IDS by detecting anomalies, but classical ML methods often struggle with high-dimensional data and evolving threats. Quantum machine learning (QML) has been proposed as a potential paradigm to overcome some of these limitations, but is constrained by noisy intermediate-scale quantum (NISQ) challenges, affecting quality. This study systematically evaluates three QML models, Pegasos Quantum Support Vector Classifier (QSVC), Variational Quantum Classifier (VQC), and a Hybrid Quantum–Classical Neural Network (HQNN), for network anomaly detection. The models were optimized and tested on the ToN_IoT and NSL-KDD datasets using IBM quantum simulators under both ideal and noisy conditions. Performance was analyzed through the F1-score distribution as a function of circuit complexity, revealing how entanglement and noise affect robustness across different backends. Comparisons with classical models contextualize the current maturity of QML for cybersecurity, while computational time was used as an indicator of model complexity to explore accuracy–efficiency trade-offs. Among all configurations, Pegasos-QSVC achieved the best results, with 94.60% accuracy and an F1-score of 94.13%. The findings provide practical guidelines for designing noise-resilient QML models and highlight their potential for reliable intrusion detection under realistic quantum conditions.
Quantum Machine Learning (QML) has emerged as an interdisciplinary field that harnesses the potential of quantum computing to revolutionize classical machine learning paradigms. This paper presents a benchmarking study focused on four quantum classifier algorithms: Quantum Support Vector Classifier (QSVC), Variational Quantum Circuit (VQC), Quantum Neural Network Classifier (QNNC) and Pegasos Quantum Support Vector Classifier (PegasosQSVC). These quantum classifiers hold promise for unlocking quantum advantages in classification tasks. However, a systematic assessment of their performance across diverse datasets is yet to be fully explored. In this research study, we evaluate and compare the classification performance of quantum machine learning classifiers on various datasets (Breast Cancer, Diabetes, and Titanic) and compare them to a classical model. Our findings revealed that PegasosQSVC consistently maintained high performance across all datasets with the highest average accuracy, recall, and F1-score values of 0.81, 0.72, and 0.71, respectively, followed by VQC, followed by VQC, while QSVC and QNNC perform variably. Nevertheless, QSVC was found to balance performance and training time, achieving an average accuracy of 0.81 and a training time of 177 seconds. Overall, the classical model surpassed QML models as it was up to 10% higher in average accuracy.