# Beyond Classical AI: Detecting Fake News with Hybrid Quantum Neural Networks
## 1 Introduction  
Fake news refers to intentionally false, misleading, or distorted information presented as factual content on digital platforms. In recent years, the spread of fake news has increased dramatically, particularly due to the influence of social media platforms. On networks such as Facebook, Twitter, and Instagram, information circulates rapidly and often without verification, leading to significant societal issues, including public misinformation, social polarization, election manipulation, and threats to public health. During the COVID-19 pandemic, a wide range of misinformation--such as anti-vaccine narratives and pseudoscientific treatment claims--spread globally, prompting the World Health Organization (WHO) to designate this phenomenon as an \"infodemic\", referring to an overabundance of information, both accurate and false, that makes it difficult for individuals to find trustworthy sources [1]. The profound and destructive consequences of fake news dissemination have made its automatic detection a critical challenge not only from a technical standpoint, but also from a broader societal perspective. Fake news detection is a problem that lies at the intersection of natural language processing (NLP) and machine learning (ML) [2]. In this context, a wide range of methods have been proposed. From the perspective of text representation, these approaches include the following:  
* TF-IDF (Term Frequency-Inverse Document Frequency). * N-gram representations. * Embedding-based methods, such as Word2 Vec [3] and GloVe [4]. Pre-trained language models, such as BERT [5], have also been widely adopted [6; 7; 8; 9; 10]. From the modeling perspective, the following approaches have been frequently employed:
* Traditional machine learning algorithms, such as Logistic Regression, Support Vector Machines (SVMs), and Random Forest classifiers. * Deep learning architectures, including Multilayer Perceptrons (MLPs), convolutional neural networks (CNNs), Long Short-Term Memory (LSTM) networks, and BERT-based classifiers [11; 12; 13; 14]. Although the performance of these models is often dependent on the dataset and the quality of feature engineering, high accuracy scores can generally be achieved. However, most classical approaches present several limitations:
* They are often constrained in their ability to capture the complex, nonlinear relationships within data. * Their recall performance may significantly decline in the presence of imbalanced datasets, particularly when fake news examples are underrepresented. * Large-scale models such as BERT demand substantial computational resources, which may limit their practical applicability in resource-constrained environments. These limitations have motivated the exploration of alternative learning paradigms. Quantum computing is one such paradigm, offering computational models that are fundamentally grounded in the laws of quantum mechanics and capable of surpassing classical computation in specific problem domains. By leveraging quantum bits (qubits), information units that exhibit uniquely quantum properties, such as superposition and the entanglement certain classes of problems, can potentially be solved exponentially faster than they can using classical machines [15]. Quantum Machine Learning (QML) has emerged as a promising approach in this context. QML models enable the embedding of classical data into quantum circuits, allowing for information to be processed within a Hilbert space, an alternative computational domain characterized by the principles of quantum mechanics. This paradigm shift offers several advantages:  
* The ability to learn complex patterns with fewer trainable parameters. * The construction of nonlinear decision boundaries through inherently quantum transformations. * The potential for more effective generalization with limited data, owing to the expressive capacity of quantum systems [16].