论文标题
通过重复的量子相估计测量值
Resource-frugal Hamiltonian eigenstate preparation via repeated quantum phase estimation measurements
论文作者
论文摘要
哈密顿特征状态的制备对于量子计算中的许多应用至关重要。可以做到这一点的效率具有关键利益。规范方法利用量子相估计（QPE）算法。我们采用该方法的变体的想法来实施一种资源柔和的迭代方案，并为各种可用信息和工具的复杂性（仿真时间成本）提供分析界。我们提出并表征涉及对目标哈密顿量的修改以提高整体效率的扩展。然后，通过准备Lih和H $ _2 $的第二个量化的哈密顿人的基态来证明所提出的方法和边界；我们使用模拟量子计算机报告了理想和嘈杂实现的性能。融合通常比边界所建议的要快得多，而定性特征则被验证。
The preparation of Hamiltonian eigenstates is essential for many applications in quantum computing; the efficiency with which this can be done is of key interest. A canonical approach exploits the quantum phase estimation (QPE) algorithm. We adopt ideas from variants of this method to implement a resource-frugal iterative scheme, and provide analytic bounds on the complexity (simulation time cost) for various cases of available information and tools. We propose and characterise an extension involving a modification of the target Hamiltonian to increase overall efficiency. The presented methods and bounds are then demonstrated by preparing the ground state of the Hamiltonians of LiH and H$_2$ in second quantisation; we report the performance of both ideal and noisy implementations using simulated quantum computers. Convergence is generally achieved much faster than the bounds suggest, while the qualitative features are validated.
