# Hybrid Quantum-Classical Deep Neural Networks Based Smart Contract Vulnerability Detection
## 5 Results  
In this study, we analyzed the results of six vulnerability detection tools (Mythril, Osiris, Oyente, Securify, Slither, Smartcheck) that examine seven vulnerability types (access control, arithmetic, front-running, reentrancy, denial of service, time manipulation, unchecked low calls). The results obtained in the study are presented as follows. The classification metrics for access control vulnerability are presented in Table 4. The classification results indicate that Slither consistently outperforms Mythril across all models. The 2-qubit and 4-qubit models achieve higher accuracy and recall than the DNN model,  
Figure 4: Basic Entangling Layer quantum circuit [74]. Figure 3: Bloch sphere [74]. suggesting that quantum-assisted architectures improve vulnerability detection. While the 4-qubit model provides slight performance gains over the 2-qubit model, the difference is minimal. For Mythril, both quantum models (2-qubit and 4-qubit) perform similarly, but the DNN model shows lower accuracy and recall, reinforcing the effectiveness of quantum-enhanced methods. Overall, quantum models exhibit stronger precision-recall balance, making them more effective in detecting access control vulnerabilities. The DNN model, while competitive, lags behind, particularly in recall, suggesting a potential trade-off between classical and quantum approaches. Table 5 presents classification results for reentrancy vulnerabilities, where Slither again shows the highest detection performance with an F1-score of 0.944 and an accuracy of 0.918. Mythril and Securify also perform well, achieving F1-scores of 0.927 and 0.910, respectively. When comparing model architectures, the 2-qubit and 4-qubit models outperform the DNN model in nearly all cases. The 4-qubit model slightly improves recall values, particularly for Mythril-Slither (0.963 recall, 0.943 accuracy), showing that increasing qubits enhances detection. The DNN model performs the weakest, especially in recall, which is crucial for identifying reentrancy attacks. This suggests that quantum-enhanced architectures may provide a more robust approach to detecting critical security threats like reentrancy. Table 6 presents the classification results for arithmetic vulnerabilities, where tool combinations outperform individual tools. The Osiris + Oyente + Smartcheck combination achieved the highest detection performance with 0.956 accuracy and 0.957 F1-score, confirming that using multiple tools enhances vulnerability detection. Among individual tools, Oyente performed the best (0.938 accuracy, 0.966 F1-score), followed by Smartcheck (0.926 accuracy, 0.944 F1-score). Mythril had the lowest performance (0.782 accuracy, 0.802 F1-score), showing that it struggles with arithmetic vulnerabilities. When comparing model architectures, the 4-qubit model generally outperformed the 2-qubit and DNN models, especially in precision and recall. Quantum-enhanced models performed significantly better than the DNN model, which had the lowest recall across all tool configurations. The results indicate that combining tools improves detection reliability, and quantum-enhanced models provide superior classification performance compared to classical deep \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & & & \multicolumn{3}{c}{**2 Qubit**} & \multicolumn{3}{c}{**4 Qubit**} & \multicolumn{3}{c}{**DNN**} \\ \hline
**Vulnerability** & **Tool** & **Acc** & **Pre** & **Re** & **F1** & **Acc** & **Pre** & **Re** & **F1** & **Acc** & **Pre** & **Re** & **F1** \\ \hline \multirow{3}{*}{Access Control} & Mythril & 0.915 & 0.941 & 0.888 & 0.913 & 0.917 & 0.941 & 0.893 & 0.916 & 0.872 & 0.870 & 0.879 & 0.874 \\  & Slither & 0.924 & 0.903 & 0.949 & 0.926 & 0.920 & 0.928 & 0.912 & 0.920 & 0.854 & 0.840 & 0.874 & 0.857 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification metrics for access control vulnerability.