# Hybrid Quantum-Classical Deep Neural Networks Based Smart Contract Vulnerability Detection
## 1 Introduction
Similarly, the Parity Wallet vulnerability in 2017 exposed a multi-signature wallet flaw, leading to over $150 million worth of Ether being permanently frozen [10]. Another significant exploit occurred in 2022 when the Wormhole Bridge was hacked, resulting in the loss of approximately $320 million due to a flaw in its smart contract validation mechanism [13]. More recently, the Ronin Bridge hack in 2022 exploited validator weaknesses, allowing attackers to drain over $600 million from Axie Infinity's network [14]. Traditional methods for detecting these vulnerabilities often involve formal verification and static analysis tools tailored for the EVM and Solidity [10]. While these methods are effective to some extent, they may struggle with scalability and the complexity of detecting sophisticated or novel attack vectors inherent in the Ethereum network. Machine learning (ML) has been proposed as a solution to enhance vulnerability detection by learning patterns associated with insecure code within the EVM context [15]. Classical machine learning approaches can sift through extensive datasets to pinpoint possible security weaknesses more effectively than manual techniques. However, they are limited by computational constraints and may not fully capture the intricacies inherent in smart contract vulnerabilities, especially as the Ethereum blockchain grows in size and complexity [16]. Moreover, classical machine learning models struggle with feature engineering limitations, often failing to extract complex vulnerability patterns due to reliance on predefined features [17]. They also exhibit poor generalization to novel attack vectors, making them ineffective against previously unseen exploits [18]. Quantum machine learning (QML) represents an innovative avenue for overcoming these constraints by harnessing quantum computing fundamentals--such as superposition and entanglement. Quantum computing itself, fueled by quantum bits (qubits), unlocks groundbreaking possibilities for tackling challenges that lie beyond the scope of classical systems, enabling QML to tackle computationally intensive tasks with unprecedented efficiency and scalability [19]. QML algorithms have the potential to process and analyze data at unprecedented speeds [20]. The application of QML to vulnerability detection could significantly enhance the ability to identify and mitigate security risks in smart contracts executed on the EVM, offering a new layer of protection in blockchain ecosystems [21]. Quantum Neural Networks (QNNs) offer a great advantage over classical methods by combining the power of quantum computing with ML [22]. QNNs can provide higher processing speed and accuracy, especially when processing large and complex datasets. However, the physical limitations of current quantum computers and the difficulties encountered in training QNNs are the biggest obstacles to this technology reaching its full potential. The sensitivity to noise in current quantum devices and the constraints imposed by having a small qubit count pose significant challenges, making it difficult for QNNs to implement deep, intricate architectures [17]. Additionally, QNNs face the \"barren plateau\" problem, where gradients rapidly approach zero during training. This phenomenon undermines the efficiency of optimizing model parameters and negatively impacts the training process [23]. During the transitional quantum stage often termed the Noisy Intermediate-Scale Quantum (NISQ) era, these issues are compounded by two more hurdles to creating QML models that outdo classical approaches: (1) the scarcity of locally accessible quantum data and (2) the absence of specialized quantum memory (QRAM) and sufficiently advanced quantum hardware to manage large-scale information. These barriers collectively impede the advancement of independent and robust QNNs, thereby slowing overall progress in the field [22]. HQCDNNs aim to unify the strongest features of classical methodologies with quantum frameworks, leading to more efficient and optimized models [22].