# Beyond Classical AI: Detecting Fake News with Hybrid Quantum Neural Networks
## 3 Results
### Comparison with Classical Models  
Table 3 presents the comparative analysis of the HQDNN model's performance with the test set alongside three widely used classical classifiers: Logistic Regression, the Linear Support Vector Machine (SVM), and Multilayer Perceptron (MLP). During the comparison, all the models were trained and evaluated using the same TF-IDF representations and an identical train/test split, ensuring a direct and fair comparison of classification performance. The results in Table 3 indicate that both the HQDNN models--TF-IDF-based and Transformer-augmented--exhibit a clear advantage in terms of recall, highlighting their effectiveness in detecting fake news. The original HQDNN model achieves a recall of 92.58%, while the DistilBERT-enhanced HQDNN further improves this to 94.40%, the highest among all the evaluated models. These results underscore both the models' strong ability to minimize false negatives, which is crucial in misinformation detection tasks, where missing a harmful instance can be particularly costly. Despite their superiority in recall, the HQDNN variants demonstrate lower precision and accuracy compared to the classical baselines, such as Logistic Regression and MLP. For instance, while MLP attains a precision of 66.45%, the precision of the Transformer-augmented HQDNN remains at 57.82%, reflecting the model's deliberate tendency to over-identify fake news to maximize safety. This recall-oriented trade-off is common in risk-sensitive applications and may be strategically acceptable in domains like disinformation detection and fraud prevention. In terms of F1 score, which balances precision and recall, the DistilBERT with Quantum HQDNN achieves 71.77%, slightly surpassing its TF-IDF counterpart (71.38%) and clearly outperforming the classical models, such as Logistic Regression (68.70%), the Linear SVM (68.44%), and MLP (68.52%). This suggests that integrating contextual language representations from Transformer models enhances the hybrid architecture's capacity to capture nuanced patterns in text, despite the model's relatively low-level parameter complexity. Additionally, the SetFit model--based on a lightweight transformer architecture--yields an F1 score of 64.49%, with balanced precision (63.87%) and recall (65.13%). While it underperforms compared to the HQDNN in terms of recall and the overall F1 score, its competitive precision and efficiency make it a viable CPU-optimized alternative. This result provides further insight into the trade-offs between quantum-enhanced sensitivity and transformer-based scalability in real-world applications. In summary, the Transformer-augmented HQDNN provides the best overall performance, especially in terms of recall and F1 score. These findings highlight the value of combining quantum-inspired modeling with semantically rich embeddings from pre-trained language models, resulting in a compact, yet powerful architecture that outperforms the traditional approaches in sensitive classification scenarios such as fake news detection. Below, we present the analysis of the confusion matrices corresponding to the Logistic Regression, Linear SVM, and Multilayer Perceptron (MLP) models on the test set. Each \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline HQDNN(TF-IDF) & 0.5817 & 0.5808 & 0.9258 & 0.7138 \\ HQDNN(Distillbert) & 0.5652 & 0.5782 & 0.9440 & 0.7176 \\ Logistic Regression & 0.6196 & 0.6404 & 0.7409 & 0.6870 \\ Linear SVM & 0.6164 & 0.6380 & 0.7381 & 0.6844 \\ MLP (NN) & 0.6338 & 0.6645 & 0.7073 & 0.6852 \\ SetFit (MiniLM) & 0.5959 & 0.6387 & 0.6513 & 0.6449 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of model performance with test set. model's classification behavior is examined in terms of correct and incorrect predictions, and insights are drawn regarding the precision-recall trade-off based on false positive (FP) and false negative (FN) distributions.