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arXiv 2010.03080quant-ph
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利用量子比特重置的量子比特有效纠缠谱

Qubit-efficient entanglement spectroscopy using qubit resets

Justin Yirka, Yigit Subasi

摘要:

在NISQ器件上解决较大问题的一种策略是利用电路宽度和电路深度之间的折衷。不幸的是,这种权衡仍然限制了易于处理的问题的大小,因为在噪声占主导地位之前,增加的深度通常无法实现。在这里,我们开发量子比特有效的量子算法纠缠光谱,避免这种权衡。特别是,我们开发了用于计算量子系统的密度算符的n次幂的迹线的算法,$Tr(ρ^n)$(与n阶Rényi熵相关),该算法使用比以前任何有效算法更少的量子比特,同时在存在噪声的情况下实现类似的性能,从而使NISQ设备上的更大量子系统的光谱学成为可能。我们的算法,这需要一个独立于n的量子位数,是以前的算法与宽度成比例的n,渐近差异的变种。这些新算法的关键要素是在计算过程中测量和重新初始化量子比特子集的能力,使我们能够重用量子比特并增加电路深度,而不会遭受通常的噪声后果。我们还引入了有效电路深度的概念,作为标准电路深度的推广,适用于具有量子位重置的电路。这个工具有助于解释我们的量子位高效算法的抗噪能力,并有助于设计未来的算法。我们进行了数值模拟,比较我们的算法与原始的变种,并显示它们在受到噪声时表现相似。此外,我们在霍尼韦尔系统模型H0上实验性地实现了我们的量子位高效算法之一,估计$Tr(ρ^n)$的n比以前的算法可能的大。

英文摘要:

One strategy to fit larger problems on NISQ devices is to exploit a tradeoff between circuit width and circuit depth. Unfortunately, this tradeoff still limits the size of tractable problems since the increased depth is often not realizable before noise dominates. Here, we develop qubit-efficient quantum algorithms for entanglement spectroscopy which avoid this tradeoff. In particular, we develop algorithms for computing the trace of the n-th power of the density operator of a quantum system, $Tr(ρ^n)$, (related to the Rényi entropy of order n) that use fewer qubits than any previous efficient algorithm while achieving similar performance in the presence of noise, thus enabling spectroscopy of larger quantum systems on NISQ devices. Our algorithms, which require a number of qubits independent of n, are variants of previous algorithms with width proportional to n, an asymptotic difference. The crucial ingredient in these new algorithms is the ability to measure and reinitialize subsets of qubits in the course of the computation, allowing us to reuse qubits and increase the circuit depth without suffering the usual noisy consequences. We also introduce the notion of effective circuit depth as a generalization of standard circuit depth suitable for circuits with qubit resets. This tool helps explain the noise-resilience of our qubit-efficient algorithms and should aid in designing future algorithms. We perform numerical simulations to compare our algorithms to the original variants and show they perform similarly when subjected to noise. Additionally, we experimentally implement one of our qubit-efficient algorithms on the Honeywell System Model H0, estimating $Tr(ρ^n)$ for larger n than possible with previous algorithms.

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