# Hybrid Quantum-Classical Deep Neural Networks Based Smart Contract Vulnerability Detection
## 6 Discussion  
The results of this study demonstrate the potential of HQCDNNs in the detection of vulnerabilities in smart contracts. Compared to classical machine learning models and deep learning models, HQCDNNs offer notable improvements in accuracy and efficiency, particularly in the classification of various vulnerability types such as access control, arithmetic, front-running, reentrancy, time manipulation, denial of service, and unchecked low calls. The integration of quantum computing principles into traditional deep learning methods allows for better pattern recognition within EVM opcode structures. The comparative performance of the 2-qubit and 4-qubit HQCDNN models provides insight into the contribution of quantum layers in our framework. In our experiments, both quantum-enhanced models consistently outperformed the conventional DNN baseline across multiple vulnerability types, underlining the efficacy of integrating quantum circuits with deep learning. The 4-qubit HQCDNN variant achieved slightly higher recall and F1-scores than the 2-qubit HQCDNNs (particularly for critical vulnerabilities like reentrancy and time manipulation), suggesting that increasing the number of qubits can enhance the model's ability to capture complex patterns in smart contract code. This improvement is attributed to the richer expressiveness afforded by additional qubits: more qubits allow the quantum layer to encode and entangle more features simultaneously, leading to better pattern recognition within the EVM opcode sequences. Notably, the performance gap between 4-qubit and 2-qubit models was relatively small for most metrics (e.g., only a minor increase in recall), indicating that even a modest 2-qubit quantum circuit is sufficient to confer a significant performance boost over classical methods. This finding is encouraging, as it shows that useful quantum advantages can be obtained without a large quantum system, which is practical given current hardware limitations. However, one key drawback observed in our experiments was the increased computational cost associated with the 4-qubit HQCDNN model. The training and inference times for the 4-qubit model were approximately 2 to 3 times longer than those for the 2-qubit variant. This is primarily due to the increased complexity of simulating larger quantum circuits, where the computational overhead grows exponentially with the number of qubits. While this longer runtime is expected in quantum-enhanced models, it raises practical concerns regarding scalability, especially when running on quantum simulators or near-term hardware with limited processing capabilities. Given that the performance gains from 4 qubits were only incremental compared to the 2-qubit model, the trade-off between accuracy and efficiency must be carefully considered in real-world applications. This hybrid synergy explains the superior accuracy and balanced precision-recall of our HQCDNN models, highlighting that even a small quantum component can substantially contribute to vulnerability detection performance. At the same time, the slight edge of the 4-qubit model underscores a trend that further quantum capacity can yield incremental gains, a consideration that points toward future research with higher qubit counts once technical challenges (like noise and circuit depth limitations) are mitigated. Other studies have used the smart wild dataset and different datasets. However, due to the differences in the tools they use, the number of samples they use in their studies may vary. Similarly, the types of vulnerabilities they study may have similarities and differences. Considering these findings, comparative results are presented in Table 11.  
According to these results, our HQCDNN model achieved an accuracy range of 96.4-78.2% and an F1-score range of 96.6-80.2%. This performance is superior to several classical models and deep learning models, such as those proposed by [PERSON] et al. (2022) with a maximum accuracy of 93.36% and [PERSON] et al. (2022) at 93.30%.