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.
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.
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.
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.
The spread of fake news on the virtual platform is so high that it requires efficient and scalable fake news detection systems. Recent language models based on transformers including DistilBERT have been shown to be very effective at extracting contextual semantic representation of text, but cannot express high-dimensional embedding space due to their dependency on classical classifiers. In the given work, we suggest QUATUM, a quantum-classical architecture with the incorporation of DistilBERT embeddings and variational quantum circuits to detect fake news. The suggested algorithm maps the output of transformers into a quantum Hilbert space to execute enhanced feature representation with superposition and entanglement on parameterized quantum circuits. They compare a traditional MLP-based classifier with the suggested quantum-enhanced model with the same training strategies on the IFND data set. It is shown that the QUATUM model has better performance, with the accuracy of 95.55%, and the equivalent values of the precision (96.45), recall (94.65), and F1-score (95.54). The results of these studies indicate that quantum-enhanced learning is effective to learn the complex decision boundaries and propose the possibility of using hybrid quantum-classical models to provide the next-generation fake news detection systems.
Fake reviews pose a critical challenge to consumer trust and platform integrity in digital marketplaces. This paper introduces a hybrid quantum-classical machine learning framework for detecting fake reviews, leveraging quantum enhancements to improve predictive accuracy. Using the Amazon Fake Reviews dataset, baseline performance is established with classical classifiers, including Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naive Bayes (NB), Random Forest (RF), and Logistic Regression (LR). Quantum machine learning (QML) models employing Amplitude Embedding and entanglement-driven circuits are integrated with classical models to form hybrid classifiers. A meta-classifier combining quantum and classical embeddings further boosts model performance, achieving up to 98.10 % accuracy. To ensure interpretability, eXplainable AI (XAI) techniques, such as SHAP and LIME, are applied to analyze predictions and identify key linguistic features. This work demonstrates the potential of quantum-augmented AI for trust-critical applications in e-commerce, paving the way for future advancements in quantum-enhanced machine learning.
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.
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.