使用深度遷移學習自動測定 Fluxonium 超導量子位元參數

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研究生：	龔桓萱 Kung, Huan-Hsuan
論文名稱：	使用深度遷移學習自動測定 Fluxonium 超導量子位元參數 Automatic Characterization of Fluxonium Superconducting Qubits Parameters with Deep Transfer Learning
指導教授：	林晏詳 Lin, Yen-Hsiang
口試委員:	許耀銓 Hoi, Io-Chun 王道維 Wang, Daw-Wei
學位類別：	碩士 Master
系所名稱：	理學院 - 物理學系 Department of Physics
論文出版年：	2024
畢業學年度：	112
語文別：	英文
論文頁數：	52
中文關鍵詞：	Fluxonium 、量子位元參數、自動測定量子位元參數、深度遷移學習、機器學習
外文關鍵詞：	fluxonium, qubit parameter, automatic characterization, deep transfer learning, machine learning
相關次數：	點閱：58 下載：0
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精確決定量子位元參數是成功實施量子資訊與計算應用的關鍵步驟。超導 fluxonium 量子位元有三個電路參數，EC、EL 和 EJ，這些參數決定了其能譜和躍遷特性。通過測量躍遷能譜並將其與 fluxonium 量子電路的哈密頓量進行擬合，可以獲得這三個參數。然而，傳統方法中量子位元的測定通常依賴於耗時且手動的擬合過程。

在本論文中，我們提出了一種自動化測定參數的方法。我們使用不同磁場下的能譜作為輸入特徵，首先對測量的能譜自動化的進行初步降躁和選點，利用已建立好的深度遷移學習模型，實現參數的初步估計，並以此為根基完成後續的自動化標記譜線及擬合。

我們利用此方法估算 fluxonium 量子位元的參數，從而展示這種方法的有效性。

與傳統擬合方法相比，我們的方法引入了自動化，顯著加速並提高了初始化過程的準確性，逐步淘汰了手動技術。我們也展示了，即使只有部分關鍵信息，我們的方法也能識別量子位元參數，從而減少測量光譜所需的時間。此外，我們的方法可以輕鬆擴展到其他超導量子位元或不同的固態系統進行自動化測定。這為建造大規模量子處理器鋪平了道路。

Accurate determination of qubit parameters is a critical step in the successful implementation of quantum information and computation applications. The superconducting fluxonium qubit has three circuit parameters: EC, EL, and EJ, which dominate the energy spectrum and properties of transitions. By measuring the transition spectrum and fitting it to the Hamiltonian of the fluxonium quantum circuit, these three parameters can be obtained. However, in traditional methods, the measurement of qubits typically relies on a time-consuming and manual fitting process.

In this thesis, we propose a method for automated parameter characterization. We use energy spectrem under different magnetic fields as input features. First, we perform automated preliminary noise reduction and point selection on the measured energy spectra. Using the pre-trained deep transfer learning model, we obtain an initial parameter estimation. Based on this initial gueess, we complete subsequent automated spectrum points labeling and fitting.

We use this method to estimate the parameters of a fluxonium qubit, thereby demonstrating the effectiveness of this approach.

Compared to traditional fitting methods, our approach introduces automation, significantly speeding up and improving the accuracy of the initialization process, gradually eliminating manual techniques. We also demonstrate that with only partial crucial information, this approach allows for the identification of qubit parameters, which can reduce the time required for measuring the spectrum. Moreover, our method can be easily extended to other fluxonium qubits or different solid-state systems for automated characterization. This paves the way for the construction of large-scale quantum processors.

Acknowledgements
摘要i
Abstract ii
Introduction 1
1 Quantum computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
2 Superconducting qubit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
3 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
4 Thesis arrangement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
Background and theory 7
1 Fluxonium qubit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.1 Quantum circuit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.2 Hamiltonian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.3 Dispersive readout . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.4 Transition spectrum . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2 Machine learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.1 Deep learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.2 Transfer learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.3 Deep transfer learning . . . . . . . . . . . . . . . . . . . . . . . . . . 17
Method 19
1 Readout . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2 Original fitting process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3 Post-processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
4 Machine learning model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
4.1 Training data preparation . . . . . . . . . . . . . . . . . . . . . . . . . 23
4.2 Model design and training . . . . . . . . . . . . . . . . . . . . . . . . 25
5 Label the transition line . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
6 Fitting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
Results 29
1 Machine learning model performance . . . . . . . . . . . . . . . . . . . . . . 29
2 The application on real fluxonium qubit energy spectrum . . . . . . . . . . . . 31
2.1 Original spectrum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
2.2 Post-processed data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
2.3 Machine learning prediction . . . . . . . . . . . . . . . . . . . . . . . 33
2.4 Label transition line . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
2.5 Fitting result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
Discussion 37
1 The importance of machine learning . . . . . . . . . . . . . . . . . . . . . . . 37
2 The importance of transfer learning . . . . . . . . . . . . . . . . . . . . . . . 41
3 Adjust the value of the noise filter . . . . . . . . . . . . . . . . . . . . . . . . 42
4 Reduce the time required for measuring spectrum . . . . . . . . . . . . . . . . 43
5 Minor drawback in this automatic process . . . . . . . . . . . . . . . . . . . . 44
6 Provide a general framework . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
Summary and outlook 47
1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
2 Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
References 49

                                

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