论文标题
直接估计最小门忠诚度
Direct estimation of minimum gate fidelity
论文作者
论文摘要
由于目前对构建量子计算机的兴趣,因此非常需要准确有效地表征量子门实现中的噪声。量子门的性能的关键度量是最小门的保真度,即门的保真度,在所有输入状态下最小化。通常,通过使用量子过程断层扫描(QPT)的实验过程准确地重建全栅极过程矩阵来估计最小忠诚度。然后,进行数值最小化以找到最小的保真度。但是,QPT是众所周知的昂贵的,如果目标只能估算一个数字,则似乎我们可以做得更好。在这项工作中,我们提出了一种混合数值实验方案,该方案采用了无数值最小化(GFM)和实验目标验证性估计程序,以直接估计最低保真度而无需重建过程矩阵。我们将其与确实使用QPT的QPT保真度估计的替代方案进行了比较,但直接采用了最小门忠诚为终止标准。因此,两种方法都可以视为直接估计方案。一般的理论界限表明,在QPT保真度估计上为GFM方案节省了大量资源。但是,针对特定噪声类别的数值模拟表明,这两个方案都具有相似的性能,这使我们想起在使用一般界限的特定示例时需要谨慎。但是,GFM计划随着更有效的GFM算法的开发提供了未来改善资源成本的潜力。
With the current interest in building quantum computers, there is a strong need for accurate and efficient characterization of the noise in quantum gate implementations. A key measure of the performance of a quantum gate is the minimum gate fidelity, i.e., the fidelity of the gate, minimized over all input states. Conventionally, the minimum fidelity is estimated by first accurately reconstructing the full gate process matrix using the experimental procedure of quantum process tomography (QPT). Then, a numerical minimization is carried out to find the minimum fidelity. QPT is, however, well known to be costly, and it might appear that we can do better, if the goal is only to estimate one single number. In this work, we propose a hybrid numerical-experimental scheme that employs a numerical gradient-free minimization (GFM) and an experimental target-fidelity estimation procedure to directly estimate the minimum fidelity without reconstructing the process matrix. We compare this to an alternative scheme, referred to as QPT fidelity estimation, that does use QPT, but directly employs the minimum gate fidelity as the termination criterion. Both approaches can thus be considered as direct estimation schemes. General theoretical bounds suggest a significant resource savings for the GFM scheme over QPT fidelity estimation; numerical simulations for specific classes of noise, however, show that both schemes have similar performance, reminding us of the need for caution when using general bounds for specific examples. The GFM scheme, however, presents potential for future improvements in resource cost, with the development of even more efficient GFM algorithms.