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.
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.
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.
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.
Classifying product reviews on e-commerce platforms, both real and fake, requires a model that can effectively represent text data patterns. This study aims to compare the performance of several Quantum Machine Learning methods, namely QNN, QSVC, and Hybrid Quantum kernel and Classical SVM, in classifying Amazon product reviews. The study uses a quantitative approach with a computational experimental design. Review data is represented using TF-IDF, standardized, and reduced in dimension with Principal Component Analysis (PCA) before being transformed into quantum feature space. Performance evaluation is carried out using accuracy, precision, recall, F1-Score, and MCC metrics. The experimental results show that QNN achieved the best performance with an accuracy value of 85,63%, an F1-Score of 0.9130, and an MCC of 0.5608, while QSVC and the hybrid approach achieved an accuracy of 83.23% with an MCC of 0.4331. These results indicate that QNN has more balanced classification performance.Keywords: Quantum Neural Network; Fake Review Detection; Amazon Reviews; Natural Language Processing; Quantum Machine Learning. AbstrakKlasifikasi ulasan produk pada platform e-commerce, baik ulasan asli maupun palsu, memerlukan model yang mampu merepresentasikan pola data teks secara efektif. Penelitian ini bertujuan untuk membandingkan kinerja beberapa metode Quantum Machine Learning (QML), yaitu QNN, Quantum Support Vector (QSVC), dan Hybrid Quantum kernel and Classical SVM, dalam mengklasifikasikan ulasan produk Amazon. Penelitian menggunakan pendekatan kuantitatif dengan desain eksperimen komputasional. Data ulasan direpresentasikan menggunakan TF-IDF, distandardisasi, dan direduksi dimensinya dengan Principal Component Analysis (PCA) sebelum ditransformasikan ke ruang fitur kuantum. Evaluasi kinerja dilakukan menggunakan metrik accuracy, precision, recall, F1-Score, dan MCC. Hasil eksperimen menunjukkan bahwa QNN memperoleh kinerja terbaik dengan nilai accuracy sebesar 85,63%, F1-Score 0.9130, dan MCC 0.5043, sedangkan QSVC dan pendekatan hybrid mencapai accuracy 83,23% dengan MCC 0,4331. Hasil ini menunjukkan bahwa QNN memiliki performa klasifikasi yang lebih seimbang.
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.
Quantum machine learning has emerged as a promising paradigm that leverages quantum computational principles to potentially outperform classical approaches on specific learning tasks. This paper presents a comprehensive empirical investigation comparing Quantum Machine Learning (QML), specifically Quantum Support Vector Machines (QSVM) against Classical Machine Learning (CML) baselines including Logistic Regression, K Nearest Neighbors and classical SVMs. We implement quantum kernel methods using variational quantum circuits with angle embedding, IQP and ZZ feature maps on datasets exhibiting varying structural complexity. Our experiments on synthetic quantum generated datasets (Quantum Parity, Quantum Entanglement, Quantum Interference) demonstrate that QSVM achieves superior classification accuracy with improvements ranging from 2.9 % to 60 % over the best classical baselines achieving a 55.6 % overall win rate. Notably, on the Quantum Parity dataset, QSVM consistently outperformed all classical models across multiple configurations. On real-world benchmark datasets (Breast Cancer, Iris, Wine), QSVM Angle achieved 88% accuracy on Breast Cancer compared to 76% for classical SVM Linear a 15.8 % relative improvement while matching the best classical performance on Iris(100%)and Wine(92%). All experiments were conducted using quantum circuit simulation on classical hardware. These results provide empirical evidence that quantum kernel methods offer meaningful advantages over classical approaches on datasets with complex feature interactions. We discuss the theoretical foundations of these improvements including quantum feature space expressivity and kernel alignment.
With the rapid growth of digital financial transactions, credit card fraud detection has become a critical challenge in the banking and finance sector. Traditional rule-based systems and classical machine learning (ML) models often struggle to detect complex and evolving fraud patterns, especially in highly imbalanced datasets. This paper proposes a novel hybrid approach to credit card fraud detection using Quantum Machine Learning (QML) techniques combined with classical methods. The system applies Principal Component Analysis (PCA) for dimensionality reduction and Synthetic Minority Oversampling Technique (SMOTE) to handle class imbalance. A classical Random Forest model is implemented as a baseline, while a QML model based on Variational Quantum Circuits (VQC) is developed using PennyLane. A hybrid decision engine combining predictions of both models is designed for improved performance. Experimental results demonstrate that the hybrid model achieves 96.9% accuracy, outperforming individual models by achieving better detection rates and reducing false predictions. The system is deployed via an interactive Streamlit-based dashboard. This work highlights the potential of Quantum Machine Learning in enhancing fraud detection systems in financial security applications.