# Beyond Classical AI: Detecting Fake News with Hybrid Quantum Neural Networks
## 5 Conclusions  
In this study, we proposed the HQDNN (Hybrid Quantum-Deep Neural Network), a hybrid artificial intelligence model designed for the automatic detection of fake news. The model integrates classical fully connected layers with a parameterized quantum circuit, aiming to develop a decision-making mechanism that incorporates both classical and quantum representations during learning.  
We evaluated two variants of the model using the widely recognized LIAR dataset: one based on traditional TF-IDF representations and another leveraging transformer-based contextual embeddings (DistilBERT). The comparative experiments revealed that the DistilBERT-enhanced HQDNN outperformed all the other models in recall (94.40%) and F1 score (71.76%), indicating superior sensitivity to fake news instances. Although classical models, such as MLP and Logistic Regression, achieved higher overall accuracy and precision, the HQDNN models--particularly the DistilBERT-based version--demonstrated a strategic recall-oriented decision bias, which is desirable in high-risk applications, such as misinformation detection and content moderation.  
The ablation experiment further confirmed the value of the quantum component. When the quantum layer was removed, the model's recall performance dropped drastically (from 94.40% to 47.38%), validating the quantum circuit's contribution to the model's ability to capture subtle patterns and reduce the number of false negatives.  
In addition to the classical baselines, we included the SetFit model--a recent lightweight transformer-based architecture--as a comparative benchmark. While SetFit demonstrated a balanced performance with an F1 score of 64.49%, it did not match the HQDNN in recall or overall sensitivity, further reinforcing the hybrid model's advantage in recall-critical tasks.  
Overall, this study demonstrates that quantum-enhanced models can serve as viable alternatives to the classical approaches, particularly in tasks characterized by class imbalance or asymmetric error costs. Even with a two-qubit quantum circuit, the HQDNN achieved competitive results, illustrating the promise of compact quantum neural architectures.  
For future work, several research directions are envisioned:  
* Increasing the quantum capacity by extending the circuit beyond two qubits and introducing entanglement mechanisms to better model complex feature interactions.
* Deploying on real quantum hardware (e.g., IBM Q and IonQ) to assess the performance differences between simulation and quantum execution.
* Evaluating model generalizability across alternative fake news datasets (e.g., FakeNewsNet) and multilingual settings.
* Building ensemble architectures that combine the HQDNN with classical models using techniques such as stacking or voting, enhancing robustness in decision making.  
In conclusion, this work serves as an early, but promising demonstration of hybrid quantum-deep learning in natural language processing. The findings suggest that quantum computation may soon become a practical complement to classical machine learning in domains that demand high sensitivity and low false negative rates.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article; further inquiries can be directed to the corresponding author.  
The author declares no conflicts of interest.