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.
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.
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.
Advancements in artificial intelligence (AI) and the increasing use of large language models (LLMs) havesignificantly impacted social computing, where the ease of generating and distributing text has amplifiedthe spread of misinformation and fake news. The problem addressed in this paper is the challenge ofdetecting fake news in a contextual manner, especially in the context of the vast amounts of text datagenerated on social media and other platforms. To tackle this, we propose a novel solution combiningQuantum Transfer Learning using BERT (Bidirectional Encoder Representations from Transformers)with Variational Quantum Circuits (VQCs). Our approach leverages the power of quantum computingto enhance the accuracy and reliability of fake news detection models, introducing a hybrid quantumclassical framework to improve detection performance. Through experiments on the CONSTRAINTCOVID-19 and IFND Political datasets, we demonstrate that the proposed Quantum Deep Learning(QDL) model achieves competitive performance over several classical deep learning baselines in termsof detection accuracy and contextual understanding. This paper presents a new direction for leveragingquantum computing in social computing applications, helping to develop better ways to mitigate falseinformation.
Quantum machine learning (QML) is an emerging field combining quantum computing and artificial intelligence, with promising applications in medicine and healthcare. This survey reviews more than 60 studies published between 2018 and 2025, highlighting a sharp increase in research activity, especially in the last three years. We address seven core research questions related to publication trends, the use of real quantum hardware versus simulators, quantum architectures overview, dataset types, medical domains, algorithmic frameworks, and reported results. Our analysis shows that most QML research in healthcare is conducted on simulators due to limited hardware access, and it relies on small datasets. Quantum convolutional neural network (QCNN) architectures dominate image-based medical tasks such as tumor detection, pneumonia diagnosis, and ECG interpretation, while feature-based datasets are mainly analyzed with variational quantum classifiers and quantum support vector machines. Despite hardware constraints, QML models often match or surpass classical machine learning approaches in accuracy, frequently reaching 95–99%. However, these performance statements should be qualified to recognize experimental limitations and avoid excessive optimism and should not be interpreted as definitive proof of quantum superiority at this stage. Additionally, issues with reproducibility and reporting of hardware details persist, which is a significant research gap. This review emphasizes the need for standardized benchmarks, more real hardware testing, and architecture-aware algorithm design. With the potential for accelerated diagnostics and personalized healthcare, QML represents a strategic direction for future medical research.
At the nexus of quantum computing and machine learning, quantum machine learning (QML) and quantum deep learning (QDL) have emerged as promising fields. By enhancing computational capabilities and addressing challenging data analysis problems, these approaches have the potential to revolutionise a number of domains by leveraging the special properties of quantum systems, such as superposition and entanglement. In this article, we give a thorough analysis of QML and QDL algorithms, discussing their underlying ideas, benefits over conventional models, and potential uses. We talk about the difficulties these algorithms face, such as noisy quantum hardware, constrained qubit resources, training and optimisation issues, input representation and data encoding problems, and limited interpretability. Additionally, we look at current developments and lines of inquiry that aim to address these issues, including hybrid classical-quantum methods, error-mitigating techniques, better data representation techniques, quantum transfer learning, and resource optimisation.
Quantum machine learning (QML) has emerged as a promising interdisciplinary field that integrates principles of quantum computing with machine learning techniques to address complex computational challenges. By leveraging quantum phenomena such as superposition and entanglement, QML aims to enhance learning efficiency, improve model performance, and enable the exploration of high-dimensional feature spaces that are intractable for classical methods. This paper presents a comprehensive review of recent developments in QML, covering fundamental concepts, algorithmic taxonomies, data encoding techniques, implementation challenges, and real-world applications. Key approaches, including quantum support vector machines (QSVM), variational quantum circuits (VQC), and quantum neural networks (QNN), are systematically analyzed. Furthermore, critical challenges, including noisy intermediate-scale quantum (NISQ) limitations, barren plateaus, data encoding bottlenecks, and the lack of demonstrated quantum advantage, are discussed in detail. The review also highlights emerging applications in material informatics, energy systems, healthcare, and optimization problems. Finally, future research directions are outlined, emphasizing the need for advancements in quantum hardware, scalable algorithms, hybrid frameworks, and standardized benchmarking. This work aims to provide a structured perspective on the current state of QML and to identify opportunities in deploy it effectively in solve real-world problems.