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| Title: | 透過自動編碼器進行量子錯誤緩解
Quantum Error Mitigation via Autoencoder Neural Networks |
| Authors: | 林孝道
Lin, Xiao-Dao |
| Contributors: | 許琇娟
Hsu, Hsiu-Chuan
林孝道
Lin, Xiao-Dao |
| Keywords: | 量子錯誤緩解
深度學習
卷積神經網絡
卷積自動編碼器
Quantum Error Mitigation
Deep learning
Convolutional Neural Networks
Convolutional Autoencoder |
| Date: | 2025 |
| Issue Date: | 2025-09-01 16:52:49 (UTC+8) |
| Abstract: | 近年來，量子計算受到廣泛關注，相關演算法與應用正積極發展。然而，現階段的量子硬體仍面臨多種量子噪聲的限制，包括操作閘不完美、去相干現象以及量測誤差等。為應對這些挑戰，研究者提出了多種量子錯誤緩解（Quantum Error Mitigation）方法，其中不少透過對量測機率分布進行後處理來減少誤差。儘管成效顯著，此類方法往往需要額外的硬體或計算資源。
本論文提出一種基於機器學習的量子錯誤緩解方法，在降低硬體複雜度的同時，提升量測機率分布的準確性。該方法採用最初用於圖像去噪的卷積自動編碼器（Convolutional Autoencoder），並以深度從1至18的4量子位元隨機電路為訓練資料，透過 Qiskit 模擬器分別生成理想與含噪聲的量測結果。訓練過程使用 Kullback-Leibler（KL）散度作為損失函數，採用 Adam 優化器，進行 500 個訓練週期，最終在驗證集上達到平均 95% 的降噪效果，且未出現過擬合跡象。
為進一步檢驗模型對不同量子態與演算法的適用性，本研究在格羅弗演算法（Grover's Algorithm）、量子傅立葉轉換（Quantum Fourier Transform）、Haar 隨機電路以及平凡順磁系統（Trivial Paramagnet）上進行測試。結果顯示，本方法能穩定且有效地抑制量測噪聲，顯示其在噪聲中等規模量子（NISQ）裝置中具有應用潛力。此外，鑑於真實量子電腦擁有不同的噪聲特徵，預訓練模型利用 IBM Quantum（IBMQ）提供的 Sherbrooke 量子處理器生成的小型資料集進行微調（fine-tuning），同樣獲得良好結果，顯示方法的可移植性與適應性。本次研究致力於發展實用、且基於學習的量子噪聲減緩技術。
Quantum computing has witnessed growing interest in recent years, with a variety of quantum algorithms and applications being actively explored. However, the current state of quantum hardware faces significant limitations due to various sources of quantum noise, including gate imperfections, decoherence, readout errors, and etc. To address these challenges, numerous error mitigation strategies have been proposed, typically involving post-processing of measurement probability distributions. While effective, many such approaches introduce additional hardware or computational overhead.
This thesis explores a machine learning-based method for quantum error mitigation that minimizes hardware complexity while improving the accuracy of measurement probability distributions. A convolutional neural network (CNN) autoencoder, originally developed for image denoising, was adapted for this purpose. The model was trained using data generated from 4-qubit random circuits of depths ranging from 1 to 18, simulated using Qiskit's simulated backends to obtain both ideal and noise-affected measurement data. The training process employed Kullback-Leibler divergence (KLD) as the loss function and the Adam optimizer for 500 epochs, resulting in an average error suppression of 95% across the validation dataset without signs of overfitting.
To evaluate its robustness across different quantum states and algorithms, the model was tested on a broad range of circuits, including Grover's algorithm, the Quantum Fourier Transform, Haar-random circuits, and the Trivial Paramagnet. The results demonstrated consistent and effective denoising of noisy measurement data, indicating that the autoencoder is a promising tool for error mitigation in noisy intermediate-scale quantum (NISQ) devices. Furthermore, owing to different noise characterizations on real quantum machines, the pretrained model was fine-tuned using a small dataset generated by the \texttt{Sherbrooke} backend from IBM Quantum (IBMQ), showing encouraging results and adaptability. This work contributes to the development of practical, learning-based quantum error mitigation techniques. |
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| Description: | 碩士
國立政治大學
應用物理研究所
112755009 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0112755009 |
| Data Type: | thesis |
| Appears in Collections: | [應用物理研究所 ] 學位論文
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