Quantum Computing Projects for M.E, M.Tech, Masters, MS abroad, and PhD students. These Quantum Computing ieee projects are implemented with future work and extension for final year project submission with research paper publishing. These research projects guide final year students to learn, practice, and complete their academic submissions successfully. Each project includes complete source code, project report, PPT, a tutorial, documentation, and a research paper.
Latest Quantum Computing Projects
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Efficient Quantum Image Classification Using Single Qubit Encoding
This project explores using quantum computing to classify images more efficiently. It develops a new method that uses only a single quantum bit to mimic traditional deep learning techniques. The approach reduces complexity and requires fewer resources than existing methods. Tests on common image datasets show promising accuracy, and the method could be improved further in the future. -
Variational Quantum Algorithms for the Allocation of Resources in a Cloud/Edge Architecture
This project studies how to efficiently assign computing tasks across different types of devices, from sensors to data centers and quantum computers. The researchers test quantum algorithms to solve this complex scheduling problem. They find that one algorithm, VQE, works better when carefully set up. Real quantum hardware experiments show it can handle larger problems faster than classical computers. -
Deep Learning based Efficient Edge Slicing for System Cost Minimization in Wireless Networks
This project focuses on making future wireless networks smarter and more efficient. It uses advanced computing at the network edge to manage resources and choose which users to serve. The system predicts network traffic and allocates resources in advance. Overall, it reduces costs while meeting the needs of different applications. -
A Systematic Analysis of Enhancing Cyber Security Using Deep Learning for Cyber Physical Systems
This project focuses on protecting cyber-physical systems, which are systems where computers control real-world devices. These systems are vulnerable to cyber-attacks, which are hard to detect. The project studies how deep learning can be used to identify attacks effectively. It also reviews existing methods and discusses future challenges in this area. -
A New Quantum Circuits of Quantum Convolutional Neural Network for X-Ray Images Classification
This project compares traditional image recognition methods with a new approach using quantum computing. It builds a Quantum Convolutional Neural Network to process images faster and more accurately than normal CNNs. The model was tested on image datasets like MNIST and COVIDX-CXR3. The results show that the quantum model improves both speed and accuracy in image classification.
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