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.
翻译:预计若干研究和工业领域的优势随着量子计算机的崛起而出现。然而,在量子计算机中装载古典数据的计算成本可能会限制可能的量子加速。创建任意量子状态的已知算法要求带有深度O(N)的量子电路装载一个N-维矢量。在这里,我们证明有可能用量子电路装载一个带有多孔深度的量子矢量子电路和辅助qubit的缠绕信息的量子矢量子矢量器。结果显示,我们可以使用分而治之的战略在量子设备中高效地装载数据以交换空间的计算时间。我们展示了真实量子设备的概念证明,并提出了两个用于量子机器学习的应用。我们期望这一新的装载策略能够让量子矢量任务加快速度,从而需要将大量信息装入量子设备。