利用Qsyn實作ZX-calculus對量子電路的動態優化

Abstract

量子電路優化在提高量子計算系統的效率和性能方面起著關鍵作用。本論文以整合量子邏輯閘分解層次調整和動態減縮算法為核心，對量子電路進行了全面的優化研究。為了方便實現和分析這些演算法，我們開發了一個名為Qsyn的C++工具。Qsyn提供了一個靈活的框架，可以高效地執行和探索各種優化策略。我們通過對各種測試案例的應用，展示了我們方法的有效性。實驗結果顯示，與現有方法相比，我們的方法在總量子邏輯閘數、2量子位邏輯閘數、量子電路深度等方面平均上分別達到11.5％、10.5％、14.8％的減少。取得了顯著的改善。此外，我們將Qsyn的性能與廣泛使用的PyZX工具進行了比較，觀察到執行時間大幅減少，特別是對於大規模電路。這些結果突顯了Qsyn的優越性，以及它在質量和計算效率方面優化量子電路的適用性。總體來說，本論文通過提供一個強大的工具和一個優化的工作流程，為量子電路優化領域做出了貢獻，使得電路優化更加有效和可擴展。

Quantum circuit optimization plays a crucial role in improving the efficiency and performance of quantum computing systems. In this thesis, we present a comprehensive study on optimizing quantum circuits by integrating decomposition level adjustment and dynamic reduction algorithms. To facilitate the implementation and analysis of these algorithms, we have developed a novel quantum circuit optimization framework called Qsyn in C++. Qsyn is very efficient in terms of the execution time and provides the flexibilities in exploring various optimization strategies. We demonstrate the effectiveness of our approach by applying it to a diverse set of test cases. Our experimental results show that our algorithm can lead to significant improvements in quantum circuit gate count, 2-qubit-gate count, and circuit depth. The reduction rate are 11.5%, 10.5%, and 14.8%, respectively, when compared to the existing approaches. Furthermore, Qsyn is much faster than the widely used quantum circuit optimization tool, PyZX, particularly for large-scale circuits. The results highlight the superiority of Qsyn and its suitability for optimizing quantum circuits in both quality and computational efficiency. Overall, this thesis contributes to the field of quantum circuit optimization by providing a powerful tool and an optimized workflow that enable more effective and scalable quantum circuit optimization.