# Hybrid Quantum-Classical Deep Neural Networks Based Smart Contract Vulnerability Detection
## 7 Conclusions  
This study introduces an innovative method for identifying smart contract vulnerabilities through the integration of HQCDNNs. The proposed model leverages the principles of quantum computing, specifically superposition and entanglement, to enhance feature extraction and classification efficiency. Unlike traditional deep learning and ML models, the HQCDNN framework combines classical neural networks with quantum circuits to improve detection accuracy across various vulnerability types, including access control, arithmetic, front-running, reentrancy, time manipulation, denial of service, and unchecked low calls. The research utilizes the SmartBugs Wild Dataset, a comprehensive dataset containing real-world Ethereum smart contracts analyzed for security vulnerabilities. The feature extraction process is based on TF-IDF and 2-g analysis, allowing the model to capture significant opcode patterns. The findings from the experiments indicate that the HQCDNN model attains higher accuracy compared to various classical and deep learning-based models. The integration of quantum computing principles, such as superposition and entanglement, enabled better pattern recognition in opcode structures, leading to improved accuracy and recall across various vulnerability types. Even with only 2 qubits, the HQCDNN model achieved superior results compared to classical deep learning, proving that a small quantum component can provide tangible benefits. The 4-qubit model offered additional improvements, particularly in complex vulnerability scenarios, though the gains were incremental rather than dramatic. One of the most notable challenges was the increased computational cost of the 4-qubit model, which took approximately 2 to 3 times longer to train and infer results compared to the 2-qubit version. This raises concerns about scalability, particularly in real-time or resource-constrained applications. While quantum hardware advancements may mitigate these issues in the future, current limitations in quantum processing power and noise sensitivity remain obstacles to large-scale implementation. The model's performance is particularly notable in detecting reentrancy and arithmetic vulnerabilities, which are among the most exploited security risks in blockchain applications. The comparative analysis against existing approaches highlights the potential of hybrid quantum-classical models in enhancing security analysis within the Ethereum ecosystem. Beyond smart contract vulnerability detection, HQCDNNs hold significant potential in various fields requiring high computational efficiency. In healthcare, they can enhance medical imaging analysis and genomic data processing, leading to more accurate diagnostics and personalized treatments. In cybersecurity, HQCDNNs can be leveraged for advanced threat detection and anomaly identification, improving real-time response to cyberattacks. Additionally, in finance, they can optimize risk assessment models and fraud detection systems by analyzing complex transaction patterns more efficiently than classical models. These applications highlight the versatility of hybrid quantum-classical approaches, demonstrating their value in solving complex problems across multiple industries. Future studies may explore evaluating the model on actual quantum hardware and training it with larger and more diverse datasets. Moreover, enhancing the model for real-time analysis, ensuring its compatibility with low-resource devices, and incorporating multimodal analyses (e.g., opcode patterns, transaction behaviors) could broaden its applicability. Furthermore, evaluating the model's robustness against adversarial attacks and refining its security capabilities could be key steps for future advancements in blockchain security. Conceptualization, E.U.K. and S.D. ; methodology, E.U.K. and S.D. ; formal analysis, E.U.K., S.D. and M.T. ; original draft preparation, E.U.K., S.D. and M.T. ; writing--review and editing, E.U.K., S.D. and M.T. ; administration, E.U.K. ; supervision, E.U.K. and M.T.