论文标题
Grover的搜索算法中的全局多部分纠缠动态
Global multipartite entanglement dynamics in Grover's search algorithm
论文作者
论文摘要
纠缠被认为是为什么量子算法在某些计算任务中比其经典对应物更有效的主要原因之一。 Grover搜索算法中多等级状态的全局多部分纠缠可以使用纠缠（GME）的几何量度来量化。 Rossi {\ Em等人}（Phys。Rev。A \ TextBf {87}，022331（2013））发现，纠缠动力学对于大$ n $是规模不变。也就是说，GME不取决于Qubits的数字$ n $；相反，这仅取决于迭代$ K $与总迭代的比率。在本文中，我们讨论了GME对大型$ n $的优化。我们证明``GME是比例不变的''并不总是存在。我们表明，通常可以根据GME曲线曲线的标记状态数量及其锤击权重来计算一个转折点。 GME在转折点之前是比例不变的。但是，GME在转折点之后并不规模不变，因为它也取决于$ n $和标记的状态。
Entanglement is considered to be one of the primary reasons for why quantum algorithms are more efficient than their classical counterparts for certain computational tasks. The global multipartite entanglement of the multiqubit states in Grover's search algorithm can be quantified using the geometric measure of entanglement (GME). Rossi {\em et al.} (Phys. Rev. A \textbf{87}, 022331 (2013)) found that the entanglement dynamics is scale invariant for large $n$. Namely, the GME does not depend on the number $n$ of qubits; rather, it only depends on the ratio of iteration $k$ to the total iteration. In this paper, we discuss the optimization of the GME for large $n$. We prove that ``the GME is scale invariant'' does not always hold. We show that there is generally a turning point that can be computed in terms of the number of marked states and their Hamming weights during the curve of the GME. The GME is scale invariant prior to the turning point. However, the GME is not scale invariant after the turning point since it also depends on $n$ and the marked states.
