本篇論文中介紹了量子電腦與量子機器學習發展至今的背景。提出基於量子立方域採樣的量子神經網路架構（QubeNet），也定義出量子立方域（Qube Sphere）。並利用Qube Sphere量子空間在量子資料中採樣特徵，再透過量子機器學習的方式來優化量子神經網路，使得QubeNet具備更強的採樣能力，能採樣出相異類別的重要類別特徵，達成影像辨識效果。此方法在量子神經網路領域中，首次成功地具備直接對影像進行辨識並達到良好效果的能力。
QubeNet屬於極輕量化的量子網路架構，總計只使用3 KB的權重檔，便能在兩類分類任務中，取得97.94 %的辨識正確率。QubeNet有別於現今大多數量子神經網路演算法，不僅能直接對影像進行分類任務，還符合現今量子電腦上可執行的量子位元數量，能在現行量子電腦中執行，為一個全新的量子機器學習架構概念。此外，本篇提出的架構具備可擴展性，其運算能力會隨著邏輯量子位元的數量增加而獲得指數性提升，未來大型量子電腦出現後此方法仍然可以使用，並且將獲得更好的效果。

In this thesis, We introduce the background on the development of quantum computers and quantum machine learning to date. A quantum neural network architecture (QubeNet) based on Qube Sphere sampling is proposed, and a quantum space Qube Sphere is defined. We use Qube Sphere quantum space to sample the features in quantum data, and then optimize the quantum neural network through quantum machine learning, so that QubeNet has a stronger sampling ability, and can sample important features of different categories to achieve the effect of image recognition.
QubeNet is an extremely lightweight quantum network architecture. It uses only a 3 KB weights file in total and achieves 97.94% recognition accuracy in two classes of image classification tasks. Unlike most quantum neural network algorithms today, QubeNet can not only classify images directly but also match the number of qubits that current quantum computers can perform. In addition, our proposed architecture is scalable, its computing power grows exponentially with the number of logical qubits, and will achieve better results when large-scale quantum computers emerge in the future.

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