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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 管希聖 | zh_TW |
| dc.contributor.advisor | Hsi-Sheng Goan | en |
| dc.contributor.author | 黃靜嚴 | zh_TW |
| dc.contributor.author | Jing-Yan Huang | en |
| dc.date.accessioned | 2024-08-14T16:33:03Z | - |
| dc.date.available | 2024-08-15 | - |
| dc.date.copyright | 2024-08-13 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-08-09 | - |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/94072 | - |
| dc.description.abstract | 量子運算在機器學習領域的應用越來越受到關注。在各種量子學習模型中，變分量子電路因其易於實作的特性而顯得尤其突出。透過了解這個模型的數學特性，可以發現具有可訓練的量子核函數的模型似乎有提升表現的潛力。我針對不同的問題測試了幾種具有此特性的方法，結果證實這些方法確實提高了模型的表達能力，也突破了使用固定核函數的模型的表現限制。 | zh_TW |
| dc.description.abstract | The application of quantum computing to machine learning problems has grown increasingly popular. Among the various quantum models, variational quantum circuits are particularly notable for their ease of implementation. Investigating the mathematical properties of such models reveals that those equipped with trainable quantum kernels may achieve enhanced performance. We tested several methods possessing this characteristic across different problems, and the results confirm that such methods not only improve model expressibility but also surpass the performance limits of models with fixed kernels. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-08-14T16:33:03Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-08-14T16:33:03Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員會審定書 i
Acknowledgements ii 摘要 iii Abstract iv Contents v List of Figures viii Chapter 1 Intrduction 1 Chapter 2 Motivation 4 2.1 Variational Quantum Circuits . . . . . . . . . 4 2.2 Kernel Methods . . . . . . . . . . . . . . . . 6 2.3 Representer Theorem . . . . . . . . . . . . . 7 2.4 VQCs are Kernel Methods . . . . . . . . . . . 8 2.5 Quantum Advantage . . . . . . . . . . . . . . 10 2.6 Increasing Models Complexity . . . . . . . . . 11 Chapter 3 Data Encoding Methods 13 3.1 Quantum Random Access Codes . . . . . . . . . 13 3.2 Trainable Embeddings . . . . . . . . . . . . . 14 3.3 Reduced Trainable Embeddings . . . . . . . . . 16 3.4 Data reuploading . . . . . . . . . . . . . . . 16 Chapter 4 Experiments 18 4.1 Frozen Lakes . . . . . . . . . . . . . . . . . 18 4.1.1 Environment . . . . . . . . . . . . . . . . 19 4.1.2 Training and Performance Estimation . . . . 20 4.1.3 Deep Q-Network . . . . . . . . . . . . . . . 20 4.1.4 Data Encoding . . . . . . . . . . . . . . . 23 4.2 MNIST Handwritten Digits . . . . . . . . . . . 23 4.2.1 Environment . . . . . . . . . . . . . . . . 23 4.2.2 Training and Performance Estimation . . . . 24 4.2.3 Algorithm . . . . . . . . . . . . . . . . . 25 4.2.4 Data Encoding . . . . . . . . . . . . . . . 26 4.3 Breast Cancer Dataset . . . . . . . . . . . . 27 4.3.1 Environment . . . . . . . . . . . . . . . . 27 4.3.2 Training and Performance Estimation . . . . 27 4.3.3 Algorithm . . . . . . . . . . . . . . . . . 28 4.3.4 Data Encoding . . . . . . . . . . . . . . . 28 Chapter 5 Results 29 5.1 Frozen Lake . . . . . . . . . . . . . . . . . 29 5.2 MNIST Handwritten Digits . . . . . . . . . . . 31 5.3 Breast Cancer Dataset . . . . . . . . . . . . 33 Chapter 6 Discussion 36 Chapter 7 Conclusion 38 References 40 | - |
| dc.language.iso | en | - |
| dc.subject | 量子機器學習 | zh_TW |
| dc.subject | 離散資料 | zh_TW |
| dc.subject | 量子變分線路 | zh_TW |
| dc.subject | 量子編碼 | zh_TW |
| dc.subject | 核方法 | zh_TW |
| dc.subject | quantum machine learning | en |
| dc.subject | quantum encoding | en |
| dc.subject | variational quantum circuit | en |
| dc.subject | kernel method | en |
| dc.subject | discrete data | en |
| dc.title | 可訓練的核函數在量子變分線路中的效益 | zh_TW |
| dc.title | Benefits of Trainable Kernels in Variational Quantum Circuits | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 林澤;林俊達;劉子毓 | zh_TW |
| dc.contributor.oralexamcommittee | Che Lin;Guin-Dar Lin;Tzu-Yu Liu | en |
| dc.subject.keyword | 量子機器學習,量子編碼,量子變分線路,核方法,離散資料, | zh_TW |
| dc.subject.keyword | quantum machine learning,quantum encoding,variational quantum circuit,kernel method,discrete data, | en |
| dc.relation.page | 43 | - |
| dc.identifier.doi | 10.6342/NTU202402911 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2024-08-12 | - |
| dc.contributor.author-college | 理學院 | - |
| dc.contributor.author-dept | 物理學系 | - |
| 顯示於系所單位： | 物理學系 | |
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