DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김도훈 | - |
dc.date.accessioned | 2023-08-31T16:35:07Z | - |
dc.date.available | 2023-08-31T16:35:07Z | - |
dc.date.issued | 2023 | - |
dc.identifier.other | OAK-2015-10212 | - |
dc.identifier.uri | http://postech.dcollection.net/common/orgView/200000693156 | ko_KR |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/118409 | - |
dc.description | Master | - |
dc.description.abstract | This thesis presents the Quantum Latent Generative Adversarial Network (QL GAN) framework to address the scalability issue of quantum GAN-based image gen eration methods. The QLGAN framework comprises two quantum machine learning models: a quantum autoencoder that compresses the quantum state into fewer qubits and a quantum GAN that learns the distribution of compressed quantum states. Ad ditionally, this thesis proposes the Quantum Image Autoencoder (QIAE), which is optimized for quantum probability image encoding. A bidirectional loss function is introduced to address the mismatch between loss and quality in image reconstruction. Experimental results shows that QIAE achieves state-of-the-art performance on image compression and reconstruction tasks over MNIST and FMNIST datasets. Further more, the combination of QLGAN and QIAE generates high-quality, high-resolution quantum images without the need for classical post-processing. | - |
dc.language | eng | - |
dc.publisher | 포항공과대학교 | - |
dc.title | High-Resolution Image Generation using Quantum Generative Adversarial Networks and Quantum Autoencoders | - |
dc.title.alternative | 양자 생성적 적대 신경망과 양자 오토인코더를 활용한 고해상도 이미지 생성 | - |
dc.type | Thesis | - |
dc.contributor.college | 전자전기공학과 | - |
dc.date.degree | 2023- 8 | - |
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