量子計算與機器學習是現今科技發展備受矚目的兩大領域，我們在基於傳統機器學
習的神經網絡架構之下，將其轉換成量子電路的型式，並且利用 IBM的量子電腦來
呈現。本篇論文主要分為三個主題:
1. 對神經網絡做重整化的處理，以此使用較少的神經元來表達相同的資訊量，藉此
將量子糾纏的特性引入到神經網絡的架構當中。 討論糾纏性質能夠在神經網絡架構
當中被轉移的現象，分析不同路徑以及不同量子體積之間對於轉移成功率的影響。
2. 我設計了三種實驗方式來討論基於不同量子體積之下，改變量子位元數量、計算
深度，對於量子電腦效能以及計算準確率的影響。
3. 為了尋找量子電腦較傳統電腦的優勢，我設計了一個類迷宮的搜尋演算法，利用
量子力學中波的疊加性以及糾纏的特性來詮釋資訊傳遞與共享的過程。

Both quantum computing and machine learning are high-profile in current technology development. We transpose the neural network model into the quantum mode with a quantum circuit and run the algorithms on IBM quantum computers. There are three main topics in my thesis: First, we try to use fewer neurons to express the same information contained in the original model by applying the renormalization method to the neural network. Therefore, we introduce the quantum entanglement to our new model. We consider that the entangled property can be transferred through a neural network model in a quantum circuit, and show how the fidelity will be affected by different paths and different quantum volumes. Second, we design three different ways to analyze the performance and fidelity of quantum computers under different quantum volumes by changing the number of qubits and depth of the circuit. Finally, to find the quantum advantage compared to classical computers, we make a maze-like search algorithm based on superposition and entanglement to demonstrate the process of information sharing.

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