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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 江介宏 | zh_TW |
| dc.contributor.advisor | Jie-Hong Roland Jiang | en |
| dc.contributor.author | 劉承瀚 | zh_TW |
| dc.contributor.author | Cheng-Han Liu | en |
| dc.date.accessioned | 2025-11-27T16:12:59Z | - |
| dc.date.available | 2025-11-28 | - |
| dc.date.copyright | 2025-11-27 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-09-02 | - |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/101086 | - |
| dc.description.abstract | 變分量子本徵值計算(VQE)是一種可應用於化學模擬的量子演算法,其中需要計算許多包利字符串(Pauli strings)的期望值,相較於每個量子電路都只計算一個包利字符串的期望值,人們通常會透過分群量測(grouped measurement)來減少量測量子電路的次數。不過每一群的重要性不盡相同,較重要的群理應分配到較多的測量次數,而且各群之間還能允許有重疊的包利字符串,因此本文主要探討該如何進行分群還有每群應該分配多少測量次數才能盡可能的使用固定的測量數目來獲得更精確的結果。
本文主要改良在於提出一個精確公式來計算該如何分配量測次數給所有的完全子圖(clique partition),並且基於此公式設計一個演算法來利用量測結果來計算共變異數資訊以尋找更好的完全子圖集合,此外重疊的包利字符串並不是選愈多愈好,因此我們也設計一套流程來選擇想要的重疊包利字符串。實驗顯示,相比於最新的先前方法AEQuO,我們的演算法能夠在更短的時間找到更好的結果。此研究有助於減少VQE演算法所需的量測數目,此研究也可以延伸擴展至其他需要求取大量期望值的量子演算法。 | zh_TW |
| dc.description.abstract | The variational quantum eigensolver (VQE) is a quantum algorithm used in chemical simulations, specifically for calculating the expectation values of various Pauli strings. Typically, instead of computing the expectation value of each Pauli string individually for every quantum circuit, researchers reduce the number of quantum circuits by employing grouped measurements. However, the significance of each group can vary; more critical groups necessitate a larger number of measurements, and overlapping Pauli strings are permitted between groups. Consequently, this paper focuses on how to effectively conduct grouping and determine the appropriate number of measurement counts assigned to each group to achieve more accurate results with a fixed number of measurements.
The main contribution of this paper lies in proposing a precise formula for allocating measurement counts to the given clique partition, along with the designed algorithm based on this formula. This algorithm uses the measurement results to calculate covariance information, which helps identify improved clique coverings. Additionally, to avoid excessive selection of overlapping Pauli strings, we have developed a process for down-sampling the redundant overlapping information. Experiments demonstrate that our algorithm outperforms the latest method, AEQuO, by delivering better results in a shorter time. This research aids in reducing the number of measurements required by the VQE algorithm and can be extended to other quantum algorithms that have a requirement to obtain the expectation values of numerous Pauli strings. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-11-27T16:12:59Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-11-27T16:12:59Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Verification Letter from the Oral Examination Committee i
Acknowledgements iii 摘要 v Abstract vii Contents ix List of Figures xiii List of Tables xvii Denotation xix Chapter 1 Introduction 1 1.1 Problem Formulation 2 1.2 Our Contributions 3 1.3 Thesis Organization 4 Chapter 2 Preliminaries 5 2.1 Quantum Computation Framework 5 2.2 Variational Quantum Eigensolver (VQE) 6 2.3 Grouped Measurement and Clique Partition 8 2.3.1 Overlapping Clique Covering 16 2.3.2 Covariance of Pauli Strings 16 2.3.3 Clique Covering Algorithms 18 2.3.3.1 Sorted Insertion (SI) 20 2.3.3.2 Iterative Coefficient Splitting (ICS) 21 2.3.3.3 Adaptive Estimation of Quantum Observables (AEQuO) 23 2.3.4 Synthesis of Measurement Circuit 24 Chapter 3 Variance-aware grouped measurement and dynamic shot allocation 27 3.1 Analytical Solution of Shot Allocation for Clique Partition 27 3.1.1 Optimal Allocation of Shot Budget 30 3.1.2 When Clique Covering is a Partition 31 3.1.3 Consideration of Downsampling 34 3.2 Whole Algorithm 36 3.2.1 Clique Covering Update 40 3.2.2 Dynamic Shot Allocation 43 3.2.3 Downsampling 45 3.2.4 Comparison with Previous Works 46 Chapter 4 Technicalities 49 4.1 Iterations of Updating Clique Covering 49 4.2 Effects of Downsampling 52 4.3 Attempt of Applying Simulated Annealing 53 Chapter 5 Experiment and Evaluation 55 5.1 Experimental Results on AEQuO Benchmark Cases 55 5.1.1 Main Results 56 5.1.2 Supplemental Results: Different Clique Partition Conditions 59 5.2 Experimental Results on Pennylane Benchmarks Cases 61 Chapter 6 Application to the whole task of VQE 63 6.1 High Allocation Budget 65 6.2 Low Allocation Budget 70 Chapter 7 Conclusion 75 References 79 Appendix A — Supplement of Expectation Value of Pauli String 85 A.1 Expectation Value Follows Bernoulli Distribution 85 Appendix B — Classical Shadow 87 Appendix C — Overview of the MEWCat Algorithm 89 C.1 Overview of the MEWCat Search Algorithm 89 C.2 Innovations of MEWCat Algorithm 91 Appendix D — Experimental Settings 93 D.1 Experimental Settings 93 | - |
| dc.language.iso | en | - |
| dc.subject | 期望值估計 | - |
| dc.subject | 分群測量 | - |
| dc.subject | 變異數感知 | - |
| dc.subject | Expectation value estimation | - |
| dc.subject | Grouped measurement | - |
| dc.subject | Variance-aware | - |
| dc.title | 具變異數感知之分組量測與動態測量配置:應用於變分量子本徵值計算 | zh_TW |
| dc.title | Variance-Aware Grouped Measurement and Dynamic Shot Allocation for VQE | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 114-1 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 邱大維;李建模;許琇娟 | zh_TW |
| dc.contributor.oralexamcommittee | Dah-Wei Chiou;Chien-Mo Li;Hsiu-Chuan Hsu | en |
| dc.subject.keyword | 期望值估計,分群測量變異數感知 | zh_TW |
| dc.subject.keyword | Expectation value estimation,Grouped measurementVariance-aware | en |
| dc.relation.page | 94 | - |
| dc.identifier.doi | 10.6342/NTU202502451 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2025-09-02 | - |
| dc.contributor.author-college | 重點科技研究學院 | - |
| dc.contributor.author-dept | 積體電路設計與自動化學位學程 | - |
| dc.date.embargo-lift | 2030-06-30 | - |
| 顯示於系所單位: | 積體電路設計與自動化學位學程 | |
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