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arXiv 2301.04201quant-ph
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随机自适应量子态制备

Randomized adaptive quantum state preparation

Alicia B. Magann, Sophia E. Economou, Christian Arenz

AI总结:

本研究提出一种无需经典优化、以随机性为核心的自适应量子态制备方法,通过带随机切空间方向的梯度测量反馈构建电路,从理论和数值上验证了几乎所有初始态均可收敛,并推导代价变化下界以分析算法大规模适用性。

摘要:

我们提出了一种自适应量子态制备方法,该方法将随机性作为核心组成部分,且无需经典优化。该方法通过自适应构建的量子电路最小化代价函数以制备目标量子态,每一步自适应操作都基于梯度测量的反馈,其中相关的切空间方向是随机化的。我们提供了理论论证与数值证据,表明几乎所有初始态都能收敛到目标态。我们研究了不同的随机化流程,并推导了期望代价函数变化的下界,这有助于建立与贫瘠高原(barren plateaus)的关联,还可用于评估该算法在大规模问题上的适用性。

英文摘要:

We develop an adaptive method for quantum state preparation that utilizes randomness as an essential component and that does not require classical optimization. Instead, a cost function is minimized to prepare a desired quantum state through an adaptively constructed quantum circuit, where each adaptive step is informed by feedback from gradient measurements in which the associated tangent space directions are randomized. We provide theoretical arguments and numerical evidence that convergence to the target state can be achieved for almost all initial states. We investigate different randomization procedures and develop lower bounds on the expected cost function change, which allows for drawing connections to barren plateaus and for assessing the applicability of the algorithm to large-scale problems.

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。

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