基于球面 Clebsch 波函数的速度场量子态制备
Quantum state preparation for a velocity field based on the spherical Clebsch wave function
AI总结：
本文提出利用球面 Clebsch 波函数和变分量子算法，通过参数化受控旋转门将给定速度场制备为量子态，为流体动力学量子模拟提供支持。
摘要：
我们提出了一种通过球面 Clebsch 波函数（SCWF）为给定速度场（例如流体动力学中的速度场）制备量子态的方法。利用 SCWF 的逐点归一化约束，我们构建了一个由参数化受控旋转门组成的变分拟设。采用变分量子算法，我们迭代优化电路参数，将目标速度场转换为 SCWF 及其对应的离散量子态，从而支持后续的流体动力学量子模拟。对一维和二维流场的验证证实了该方法的准确性和鲁棒性，凸显了其在处理多尺度和多维速度场方面的有效性。我们的方法能够捕捉源、汇和鞍点等关键流动特征。此外，它还能为各种矢量场生成 SCWF，随后可通过 SCWF 演化应用于量子模拟中。
英文摘要：
We propose a method for preparing the quantum state for a given velocity field, e.g., in fluid dynamics, via the spherical Clebsch wave function (SCWF). Using the pointwise normalization constraint for the SCWF, we develop a variational ansatz comprising parameterized controlled rotation gates. Employing the variational quantum algorithm, we iteratively optimize the circuit parameters to transform the target velocity field into the SCWF and its corresponding discrete quantum state, enabling subsequent quantum simulation of fluid dynamics. Validations for one- and two-dimensional flow fields confirm the accuracy and robustness of our method, emphasizing its effectiveness in handling multiscale and multidimensional velocity fields. Our method is able to capture critical flow features like sources, sinks, and saddle points. Furthermore, it enables the generation of SCWFs for various vector fields, which can then be applied in quantum simulations through SCWF evolution.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
