PegasosQSVM: A Quantum Machine Learning Approach for Accurate Fake News Detection
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 propos...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
|
| Series: | Applied Artificial Intelligence |
| Online Access: | https://www.tandfonline.com/doi/10.1080/08839514.2025.2457207 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1825206463391334400 |
|---|---|
| author | Mehdi Khalil Chi Zhang Zhiwei Ye Peng Zhang |
| author_facet | Mehdi Khalil Chi Zhang Zhiwei Ye Peng Zhang |
| author_sort | Mehdi Khalil |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2806d150f02b4043b7cdd17d05944d02 |
| institution | Kabale University |
| issn | 0883-9514 1087-6545 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj-art-2806d150f02b4043b7cdd17d05944d022025-02-07T09:07:13ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452025-12-0139110.1080/08839514.2025.2457207PegasosQSVM: A Quantum Machine Learning Approach for Accurate Fake News DetectionMehdi Khalil0Chi Zhang1Zhiwei Ye2Peng Zhang3School of Computer Science, Hubei University of Technology, Wuhan, Hubei, ChinaSchool of Computer Science, Hubei University of Technology, Wuhan, Hubei, ChinaSchool of Computer Science, Hubei University of Technology, Wuhan, Hubei, ChinaWuhan Fiberhome Technical Service, Fiberhome, Wuhan, Hubei, ChinaThe 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.https://www.tandfonline.com/doi/10.1080/08839514.2025.2457207 |
| spellingShingle | Mehdi Khalil Chi Zhang Zhiwei Ye Peng Zhang PegasosQSVM: A Quantum Machine Learning Approach for Accurate Fake News Detection Applied Artificial Intelligence |
| title | PegasosQSVM: A Quantum Machine Learning Approach for Accurate Fake News Detection |
| title_full | PegasosQSVM: A Quantum Machine Learning Approach for Accurate Fake News Detection |
| title_fullStr | PegasosQSVM: A Quantum Machine Learning Approach for Accurate Fake News Detection |
| title_full_unstemmed | PegasosQSVM: A Quantum Machine Learning Approach for Accurate Fake News Detection |
| title_short | PegasosQSVM: A Quantum Machine Learning Approach for Accurate Fake News Detection |
| title_sort | pegasosqsvm a quantum machine learning approach for accurate fake news detection |
| url | https://www.tandfonline.com/doi/10.1080/08839514.2025.2457207 |
| work_keys_str_mv | AT mehdikhalil pegasosqsvmaquantummachinelearningapproachforaccuratefakenewsdetection AT chizhang pegasosqsvmaquantummachinelearningapproachforaccuratefakenewsdetection AT zhiweiye pegasosqsvmaquantummachinelearningapproachforaccuratefakenewsdetection AT pengzhang pegasosqsvmaquantummachinelearningapproachforaccuratefakenewsdetection |