# Hybrid Quantum-Classical Deep Neural Networks Based Smart Contract Vulnerability Detection
## 5 Results
\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}{*}{Reentrancy} & Mythril & 0.898 & 0.913 & 0.942 & 0.927 & 0.898 & 0.878 & 0.915 & 0.896 & 0.892 & 0.869 & 0.912 & 0.890 \\  & Slither & 0.918 & 0.912 & 0.978 & 0.944 & 0.915 & 0.878 & 0.956 & 0.915 & 0.870 & 0.845 & 0.895 & 0.869 \\ \cline{1-1}  & Securify & 0.907 & 0.886 & 0.935 & 0.910 & 0.920 & 0.900 & 0.945 & 0.922 & 0.852 & 0.859 & 0.843 & 0.851 \\ \cline{1-1}  & Mythril & 0.940 & 0.932 & 0.950 & 0.941 & 0.943 & 0.923 & 0.963 & 0.942 & 0.896 & 0.872 & 0.918 & 0.894 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification metrics for reentrancy vulnerability. learning approaches. This pattern suggests that hybrid quantum-classical models may offer a strong advantage for detecting arithmetic vulnerabilities. Comparing model architectures, the 4-qubit model slightly outperformed the 2-qubit model, showing small but consistent gains in accuracy and F1-score. The DNN model, while competitive, had the lowest precision and recall, indicating that classical deep learning struggles more with time-based exploits. These results suggest that quantum-enhanced models (especially with 4 qubits) provide more reliable detection of time manipulation vulnerabilities, and Oyente remains the most effective tool for identifying such exploits. Table 8 shows that Security achieved the highest performance in detecting front-running vulnerabilities (0.923 accuracy, 0.943 F1-score), making it the most effective tool for identifying transaction order manipulation in DeFi applications. Among model architectures, the 2-qubit model performed best, slightly outperforming the 4-qubit model, while \begin{table}
\begin{tabular}{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 Mythril & 0.782 & 0.722 & 0.903 & 0.802 & 0.837 & 0.788 & 0.905 & 0.842 & 0.806 & 0.749 & 0.899 & 0.817 \\ Osiris & 0.928 & 0.923 & 0.990 & 0.955 & 0.911 & 0.906 & 0.911 & 0.908 & 0.891 & 0.878 & 0.898 & 0.888 \\ Oyente & 0.938 & 0.942 & 0.992 & 0.966 & 0.825 & 0.787 & 0.874 & 0.828 & 0.790 & 0.775 & 0.793 & 0.784 \\ Smartcheck & 0.926 & 0.943 & 0.945 & 0.944 & 0.908 & 0.901 & 0.908 & 0.905 & 0.864 & 0.829 & 0.902 & 0.864 \\ \hline Mythril & 0.928 & 0.933 & 0.961 & 0.947 & 0.917 & 0.887 & 0.948 & 0.916 & 0.898 & 0.856 & 0.948 & 0.900 \\ Oyente & 0.907 & 0.911 & 0.983 & 0.946 & 0.840 & 0.803 & 0.885 & 0.842 & 0.805 & 0.741 & 0.913 & 0.818 \\ Mythril & 0.927 & 0.916 & 0.938 & 0.927 & 0.832 & 0.930 & 0.934 & 0.932 & 0.854 & 0.858 & 0.844 & 0.851 \\ Arithmetic & 0.954 & 0.937 & 0.976 & 0.956 & 0.964 & 0.966 & 0.963 & 0.965 & 0.934 & 0.922 & 0.951 & 0.936 \\ \hline Oyente & 0.938 & 0.942 & 0.952 & 0.947 & 0.940 & 0.939 & 0.936 & 0.938 & 0.872 & 0.895 & 0.831 & 0.862 \\ Oyente & 0.956 & 0.941 & 0.973 & 0.957 & 0.963 & 0.952 & 0.975 & 0.963 & 0.905 & 0.911 & 0.898 & 0.905 \\ Smartcheck & & & & & & & & & & & & & \\ \hline Mythril & 0.935 & 0.940 & 0.959 & 0.950 & 0.921 & 0.893 & 0.949 & 0.920 & 0.896 & 0.862 & 0.933 & 0.896 \\ Oyente & & & & & & & & & & & & & \\ \hline Mythril & 0.926 & 0.921 & 0.932 & 0.927 & 0.929 & 0.928 & 0.930 & 0.929 & 0.864 & 0.879 & 0.846 & 0.862 \\ Smartcheck & & & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Classification metrics for arithmetic vulnerability.