量子态制备的分治算法
A divide-and-conquer algorithm for quantum state preparation
摘要：
随着量子计算机的兴起，预计将在几个研究和工业领域取得优势。然而，在量子计算机中加载经典数据的计算成本可能会对可能的量子加速造成限制。创建任意量子态的已知算法需要深度为O（N）的量子线路来加载N维向量。在这里，我们表明，它是可能的加载一个N维矢量与量子线路与多对数深度和纠缠信息的辅助量子比特。结果表明，我们可以有效地加载数据的量子设备使用分而治之的策略，以交换计算时间的空间。我们在一个真实的量子设备上演示了概念证明，并提出了量子机器学习的两个应用。我们预计，这种新的加载策略可以实现需要将大量信息加载到量子设备的任务的量子加速。
英文摘要：
Advantages in several fields of research and industry are expected with the rise of quantum computers. However, the computational cost to load classical data in quantum computers can impose restrictions on possible quantum speedups. Known algorithms to create arbitrary quantum states require quantum circuits with depth O(N) to load an N-dimensional vector. Here, we show that it is possible to load an N-dimensional vector with a quantum circuit with polylogarithmic depth and entangled information in ancillary qubits. Results show that we can efficiently load data in quantum devices using a divide-and-conquer strategy to exchange computational time for space. We demonstrate a proof of concept on a real quantum device and present two applications for quantum machine learning. We expect that this new loading strategy allows the quantum speedup of tasks that require to load a significant volume of information to quantum devices.
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