Blockchain Smart Contract Vulnerability Detection Using Quantum Convolution Neural Network

The proposed project aims to develop a Quantum Convolutional Neural Network (QCNN)-based approach to detect vulnerabilities in smart contracts, which are critical components of blockchain technology. By leveraging quantum machine learning techniques, the project seeks to enhance the accuracy and efficiency of identifying security threats in smart contracts, such as reentrancy attacks and integer overflows. This research will involve collecting and preprocessing a dataset of smart contracts, designing and implementing a QCNN model using frameworks like TensorFlow Quantum and Qiskit, and benchmarking its performance against traditional deep learning methods. The expected benefit to the participating institutions includes advancing their expertise in quantum computing and blockchain security, fostering collaboration between researchers, and contributing to the development of more secure and reliable blockchain ecosystems. This project will also provide valuable training opportunities for interns, equipping them with cutting-edge skills in quantum machine learning and cybersecurity.

Faculty Supervisor:

Ajmery Sultana

Student:

Partner:

Daffodil International University

Discipline:

Computer science

Sector:

Cyber Security; Quantum Science; Artificial Intelligence

University:

Algoma University

Program: