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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 鄭皓中 | zh_TW |
| dc.contributor.advisor | Hao-Chung Cheng | en |
| dc.contributor.author | 周家儀 | zh_TW |
| dc.contributor.author | Chia-Yi Chou | en |
| dc.date.accessioned | 2024-02-22T16:37:48Z | - |
| dc.date.available | 2024-02-23 | - |
| dc.date.copyright | 2024-02-22 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-01-31 | - |
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Parameterized quantum circuits as machine learning models. Quantum Science and Technology, 4(4):043001, 2019. [9] Maria Schuld, Ville Bergholm, Christian Gogolin, Josh Izaac, and Nathan Killo-ran. Evaluating analytic gradients on quantum hardware. Physical Review A, 99(3):032331, 2019. [10] Kosuke Mitarai, Makoto Negoro, Masahiro Kitagawa, and Keisuke Fujii. Quantum circuit learning. Physical Review A, 98(3):032309, 2018. [11] Maxwell Henderson, Samriddhi Shakya, Shashindra Pradhan, and Tristan Cook. Quanvolutional neural networks: Powering image recognition with quantum circuits, 2019. [12] Cheng Chu, Grant Skipper, Martin Swany, and Fan Chen. Iqgan: Robust quantum generative adversarial network for image synthesis on nisq devices. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1–5. IEEE, 2023. [13] Samuel A Stein, Betis Baheri, Daniel Chen, Ying Mao, Qiang Guan, Ang Li, Bo Fang, and Shuai Xu. 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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/91766 | - |
| dc.description.abstract | 在傳統的機器學習領域中，生成對抗網路已被廣泛應用於產生高解析度及多樣化影像，展現出卓越的表現能力。近年來，量子機器學習領域也出現了探討生成對抗網路的相關研究。在影像生成的應用中，一些研究提出混合式生成對抗網路，結合了經典架構的判別模型和量子架構的生成模型。其中，量子架構的生成模型是由數個子生成模型組成，每個子生成模型所產生的資料最終會合併成一張圖像。然而，在先前的研究中，這些模型若缺乏充分的訓練時間時，容易生成被人眼判別為假的圖像，例如模糊且多個類別重疊的生成影像。
在本文中，我們提出名為「混合深度卷積生成對抗網路方法」(Hybrid DCGAN) 的全新架構。在不使用任何特徵壓縮或降維的方法下，Hybrid DCGAN 可透過量子電路實現可調參數的卷積運算，並用於生成大小為16x16的MNIST資料集影像。根據生成影像的評量標準，模擬結果顯示 Hybrid DCGAN 所生成的圖片在單一或多個類別訓練資料上的品質優於先前的混合生成對抗網路，在相同架構下也能與傳統網路達到接近的分數。此外，Hybrid DCGAN 在處理高解析度及多維度的影像生成問題上也展現了潛力。 | zh_TW |
| dc.description.abstract | Generative Adversarial Networks have exhibited potential in high-resolution image generation and the ability to develop diverse synthetic data. Recent advancements in Quantum Machine Learning also explored hybrid generative structures by employing Classical Discriminators and Quantum Generators, which utilize multiple sub-generators to partially produce the image pixels. However, within finite training durations, these methods often encountered challenges in generating blurred outcomes and overlapping multi-class features that could be identified by human judgment.
In this work, we propose the “Hybrid Deep Convolutional Generative Adversarial Network” (Hybrid DCGAN), integrating convolutional computation with amplitude encoding and trainable quantum circuits. Our Hybrid DCGAN model can produce 16 by 16 MNIST datasets without adopting feature compression or dimension reduction techniques. Based on the one-class or multi-class training samples, numerical simulations demonstrate that the image quality of our scheme outperforms the previous approaches and achieves comparable benchmarks with classical counterparts. Moreover, the Hybrid DCGAN model shows potential for scalability in tackling higher resolution and multi-channel image generation tasks. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-02-22T16:37:48Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-02-22T16:37:48Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Contents
Verification Letter from the Oral Examination Committee i Acknowledgements ii 摘要 iii Abstract iv Contents vi List of Figures viii List of Tables xiii Chapter 1 Introduction 1 Chapter 2 Quantum Computation 4 2.1 Quantum State . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.2 Quantum Circuit . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.3 Measurement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.4 Quantum State Encoding method . . . . . . . . . . . . . . . . . . . 12 2.5 Variational Quantum Circuit . . . . . . . . . . . . . . . . . . . . . . 14 Chapter 3 Related Works 15 3.1 Classical Generative Adversarial Network . . . . . . . . . . . . . . . 15 3.2 Quantum Generative Adversarial Network . . . . . . . . . . . . . . 18 Chapter 4 Proposed Method 22 4.1 Hybrid Deep Convolutional Generative Adversarial Network . . . . . 22 4.2 Quantum Convolutional Neural Network . . . . . . . . . . . . . . . 25 4.2.1 General QCNN Circuit . . . . . . . . . . . . . . . . . . . . . . . . 26 4.2.2 Modified QCNN Circuit . . . . . . . . . . . . . . . . . . . . . . . 31 Chapter 5 Results and Discussion 35 5.1 Experimental Setting . . . . . . . . . . . . . . . . . . . . . . . . . . 35 5.1.1 Training Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 5.1.2 The Details of Comparative Models . . . . . . . . . . . . . . . . . 36 5.1.3 Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 5.2 Generated Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 5.2.1 Shallow Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 5.2.2 Deep Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 5.3 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 5.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 5.4.1 Method Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . 50 5.4.2 Parameter Counting . . . . . . . . . . . . . . . . . . . . . . . . . . 51 5.4.3 Multidimensional Image Generation . . . . . . . . . . . . . . . . . 53 5.4.4 Real Device Implementation . . . . . . . . . . . . . . . . . . . . . 54 Chapter 6 Conclusion 55 References 57 Appendix A — Other Simulations 62 A.1 Simulations on EMNIST and Fashion MNIST . . . . . . . . . . . . . 62 | - |
| dc.language.iso | en | - |
| dc.subject | 量子計算 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 量子機器學習 | zh_TW |
| dc.subject | 生成對抗網路 | zh_TW |
| dc.subject | 量子生成對抗網路 | zh_TW |
| dc.subject | 卷積神經網路 | zh_TW |
| dc.subject | 量子卷積神經網路 | zh_TW |
| dc.subject | 變分量子電路 | zh_TW |
| dc.subject | Variational Quantum Circuit | en |
| dc.subject | Quantum Computation | en |
| dc.subject | Quantum Convolutional Neural Network | en |
| dc.subject | Machine Learning | en |
| dc.subject | Quantum Machine Learning | en |
| dc.subject | Generative Adversarial Network | en |
| dc.subject | Quantum Generative Adversarial Network | en |
| dc.subject | Convolutional Neural Network | en |
| dc.title | 用於影像生成的混合深度卷積生成對抗網路 | zh_TW |
| dc.title | Hybrid Deep Convolutional Generative Adversarial Networks for Image Generation | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-1 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 管希聖;賴青沂 | zh_TW |
| dc.contributor.oralexamcommittee | Hsi-Sheng Goan;Ching-Yi Lai | en |
| dc.subject.keyword | 量子計算,機器學習,量子機器學習,生成對抗網路,量子生成對抗網路,卷積神經網路,量子卷積神經網路,變分量子電路, | zh_TW |
| dc.subject.keyword | Quantum Computation,Machine Learning,Quantum Machine Learning,Generative Adversarial Network,Quantum Generative Adversarial Network,Convolutional Neural Network,Quantum Convolutional Neural Network,Variational Quantum Circuit, | en |
| dc.relation.page | 66 | - |
| dc.identifier.doi | 10.6342/NTU202400279 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2024-02-03 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 電信工程學研究所 | - |
| dc.date.embargo-lift | 2026-01-30 | - |
| 顯示於系所單位： | 電信工程學研究所 | |
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