# Beyond Classical AI: Detecting Fake News with Hybrid Quantum Neural Networks
## 4 Discussion
### The Impact of the Quantum Layer  
The distinct performance gains achieved by the HQDNN model, particularly in recall, can be primarily attributed to its hybrid architecture that incorporates a parameterized quantum circuit. This quantum layer plays a critical role in transforming the feature space by enabling learning through quantum state evolutions, in addition to classical weight updates. Operating in a Hilbert space, the quantum component allows for the model to explore a richer and more expressive probabilistic landscape compared to conventional parametric surfaces. In this study, the model employs a lightweight two-qubit quantum circuit, which encodes classical TF-IDF features using RX rotation gates, applies learnable RY and RZ gates, and produces outputs through Pauli-Z measurements. Despite its compact design, this configuration facilitates the construction of highly nonlinear decision boundaries using only a minimal number of trainable parameters. This is consistent with the findings in prior literature [12; 16], which emphasize the ability of quantum circuits to efficiently represent complex data structures with fewer resources, a crucial advantage in scenarios with limited data or computational constraints. From a task-specific perspective, fake news detection often involves short, semantically dense statements that exhibit subtle linguistic patterns not easily separable using classical linear models. In such cases, the quantum circuit's ability to map inputs to highly nonlinear and probabilistic representations in a Hilbert space may facilitate the discrimination of borderline cases, particularly those prone to class ambiguity. The use of RX, RY, and RZ gates in our parameterized circuit enables flexible angular encodings that adaptively rotate the input space, generating multidimensional decision boundaries not trivially achieved by classical architectures. This transformation capability likely contributes to the superior recall performance, as the model becomes more sensitive to minority class instances, without substantially increasing the model complexity. The significance of the quantum layer's contribution became particularly evident through the ablation experiment. When the quantum component was removed (HQDNN_NoQuantum), the model's recall dropped sharply from 94.40% to 47.38%. While the accuracy and precision experienced a slight increase in the classical variant, this came at the cost of a dramatic rise in false negatives, undermining the primary objective of fake news detection. This contrast empirically validates the quantum layer's role in enhancing sensitivity to the positive class and confirms its effectiveness in reducing critical misclassifications. Furthermore, these results demonstrate that even with a minimal quantum footprint--only two qubits--the hybrid model is capable of outperforming several classical models and approaching the performance of more complex architectures like DistilBERT, all while maintaining a lower parameter count. This highlights the potential for practical deployment of hybrid quantum-classical models in tasks requiring superior recall, compact design, and resource efficiency. One possible explanation for the HQDNN model's superior recall, but comparatively lower precision and accuracy lies in the expressive nature of the quantum circuit's decision boundaries. The parameterized quantum layer operates in a Hilbert space and applies unitary transformations capable of modeling highly nonlinear and entangled feature relationships. This enhanced expressiveness enables the model to capture subtle patterns associated with the minority (positive) class--i.e., fake news instances--which often exhibit complex or ambiguous linguistic features. As a result, the HQDNN is more likely to detect such instances, thereby improving recall. However, this comes with an increased risk of over-generalization in the feature space, leading to more false positives and a drop in precision.