Hybrid Approach in Quantum Generative Adversarial Network

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dc.contributor.advisorMahdy Rahman Chowdhury
dc.contributor.authorArabindo Das Himu
dc.contributor.id1831592642
dc.coverage.departmentElectrical and Computer Engineering
dc.date.accessioned2024-05-19
dc.date.accessioned2024-05-19T05:33:44Z
dc.date.available2024-05-19T05:33:44Z
dc.date.issued2023
dc.description.abstractThis project explores the application of quantum computing techniques in the domain of generative adversarial networks (GANs) for image synthesis, specifically targeting the well-known MNIST dataset of handwritten digits. Leveraging TensorFlow Quantum (TFQ), this research delves into hybrid quantum-classical architectures, combining the power of quantum circuits with classical deep learning models. This project aims to demonstrate the potential of quantum GAN in generating synthetic images. This study's methodology includes data encoding, quantum circuit design, and classical optimization. The experimental results showcase the ability to generate fake samples. Compared with the classical counterpart, the project shows an improvement in generating samples over multiple epochs. However, after running 100 epochs, the hybrid version that uses both quantum circuits and classical neural networks shows a decreasing point in improvement compared to the classical counterpart that was able to improve over time without a decreasing point of improvement. While the current development of this project shows the capability of generating fake samples, further research is necessary to increase its performance and overcome the related issues. This tells us the potential usage of real-world applications that can be done with the capability of quantum computing on machine learning in generative tasks.
dc.description.degreeUndergraduate
dc.identifier.cd600000340
dc.identifier.print-thesisTo be assigned
dc.identifier.urihttps://repository.northsouth.edu/handle/123456789/755
dc.language.isoen
dc.publisherNorth South University
dc.rights©NSUlibrary
dc.titleHybrid Approach in Quantum Generative Adversarial Network
dc.typeProject
oaire.citation.endPage45
oaire.citation.startPage1
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