Beyond Classical AI: Detecting Fake News with Hybrid Quantum Neural Networks
The advent of quantum computing has introduced new opportunities for enhancing classical machine learning architectures. In this study, we propose a novel hybrid model, the HQDNN (Hybrid Quantum–Deep Neural Network), designed for the automatic detection of fake news. The model integrates classical f...
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MDPI AG
2025-07-01
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| Series: | Applied Sciences |
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| author | Volkan Altıntaş |
| author_facet | Volkan Altıntaş |
| author_sort | Volkan Altıntaş |
| collection | DOAJ |
| description | The advent of quantum computing has introduced new opportunities for enhancing classical machine learning architectures. In this study, we propose a novel hybrid model, the HQDNN (Hybrid Quantum–Deep Neural Network), designed for the automatic detection of fake news. The model integrates classical fully connected neural layers with a parameterized quantum circuit, enabling the processing of textual data within both classical and quantum computational domains. To assess its effectiveness, we conducted experiments on the widely used LIAR dataset utilizing Term Frequency–Inverse Document Frequency (TF-IDF) features, as well as transformer-based DistilBERT embeddings. The experimental results demonstrate that the HQDNN achieves a superior recall performance—92.58% with TF-IDF and 94.40% with DistilBERT—surpassing traditional machine learning models such as Logistic Regression, Linear SVM, and Multilayer Perceptron. Additionally, we compare the HQDNN with SetFit, a recent CPU-efficient few-shot transformer model, and show that while SetFit achieves higher precision, the HQDNN significantly outperforms it in recall. Furthermore, an ablation experiment confirms the critical contribution of the quantum component, revealing a substantial drop in performance when the quantum layer is removed. These findings highlight the potential of hybrid quantum–classical models as effective and compact alternatives for high-sensitivity classification tasks, particularly in domains such as fake news detection. |
| format | Article |
| id | doaj-art-91bdfc63f84f4820a1b6f8a9bca5b177 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-91bdfc63f84f4820a1b6f8a9bca5b1772025-08-20T03:36:02ZengMDPI AGApplied Sciences2076-34172025-07-011515830010.3390/app15158300Beyond Classical AI: Detecting Fake News with Hybrid Quantum Neural NetworksVolkan Altıntaş0Department of Computer Engineering, Engineering and Natural Sciences Faculty, Manisa Celal Bayar University, Manisa 45140, TurkeyThe advent of quantum computing has introduced new opportunities for enhancing classical machine learning architectures. In this study, we propose a novel hybrid model, the HQDNN (Hybrid Quantum–Deep Neural Network), designed for the automatic detection of fake news. The model integrates classical fully connected neural layers with a parameterized quantum circuit, enabling the processing of textual data within both classical and quantum computational domains. To assess its effectiveness, we conducted experiments on the widely used LIAR dataset utilizing Term Frequency–Inverse Document Frequency (TF-IDF) features, as well as transformer-based DistilBERT embeddings. The experimental results demonstrate that the HQDNN achieves a superior recall performance—92.58% with TF-IDF and 94.40% with DistilBERT—surpassing traditional machine learning models such as Logistic Regression, Linear SVM, and Multilayer Perceptron. Additionally, we compare the HQDNN with SetFit, a recent CPU-efficient few-shot transformer model, and show that while SetFit achieves higher precision, the HQDNN significantly outperforms it in recall. Furthermore, an ablation experiment confirms the critical contribution of the quantum component, revealing a substantial drop in performance when the quantum layer is removed. These findings highlight the potential of hybrid quantum–classical models as effective and compact alternatives for high-sensitivity classification tasks, particularly in domains such as fake news detection.https://www.mdpi.com/2076-3417/15/15/8300fake news detectionquantum machine learningnatural language processinghybrid quantum classical modeltext classification |
| spellingShingle | Volkan Altıntaş Beyond Classical AI: Detecting Fake News with Hybrid Quantum Neural Networks Applied Sciences fake news detection quantum machine learning natural language processing hybrid quantum classical model text classification |
| title | Beyond Classical AI: Detecting Fake News with Hybrid Quantum Neural Networks |
| title_full | Beyond Classical AI: Detecting Fake News with Hybrid Quantum Neural Networks |
| title_fullStr | Beyond Classical AI: Detecting Fake News with Hybrid Quantum Neural Networks |
| title_full_unstemmed | Beyond Classical AI: Detecting Fake News with Hybrid Quantum Neural Networks |
| title_short | Beyond Classical AI: Detecting Fake News with Hybrid Quantum Neural Networks |
| title_sort | beyond classical ai detecting fake news with hybrid quantum neural networks |
| topic | fake news detection quantum machine learning natural language processing hybrid quantum classical model text classification |
| url | https://www.mdpi.com/2076-3417/15/15/8300 |
| work_keys_str_mv | AT volkanaltıntas beyondclassicalaidetectingfakenewswithhybridquantumneuralnetworks |