Araujo Israel F, Park Daniel K, Petruccione Francesco, da Silva Adenilton J
Centro de Informática, Universidade Federal de Pernambuco, Recife, Pernambuco, Brazil.
Sungkyunkwan University Advanced Institute of Nanotechnology, Suwon, 16419, South Korea.
Sci Rep. 2021 Mar 18;11(1):6329. doi: 10.1038/s41598-021-85474-1.
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 exponential time advantage using 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.
随着量子计算机的兴起，有望在多个研究和工业领域带来优势。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产生限制。已知的创建任意量子态的算法需要深度为O(N)的量子电路来加载一个N维向量。在此，我们表明，利用具有多对数深度且辅助量子比特中存在纠缠信息的量子电路，有可能以指数时间优势加载一个N维向量。结果表明，我们可以使用分治策略在量子设备中高效加载数据，以计算时间换取空间。我们在真实的量子设备上展示了概念验证，并给出了量子机器学习的两个应用。我们预计，这种新的加载策略将使需要将大量信息加载到量子设备的任务实现量子加速。
