混合量子奇异谱分解用于时间序列分析

IF 4.2 Q2 QUANTUM SCIENCE & TECHNOLOGY
J. J. Postema, P. Bonizzi, G. Koekoek, R. L. Westra, S. J. J. M. F. Kokkelmans
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引用次数: 0

摘要

传统的数据分析需要计算能力,这在大数据时代变得难以处理。时间序列分析的一项基本任务是从噪声时间序列中提取物理上有意义的信息。为此目的而设计的一种算法是奇异谱分解(SSD),这是一种自适应方法,允许从非平稳和非线性时间序列中提取窄带分量。该算法的主要计算瓶颈是奇异值分解(SVD)。量子计算可以通过优越的缩放定律促进这一领域的加速。我们通过分配奇异值分解子程序到量子计算机来提出量子固态硬盘。研究了该混合算法在近期混合量子计算机上实现的可行性和性能。在这项工作中,我们表明,通过采用随机SVD,我们可以在其中一个电路上施加量子位限制以提高可伸缩性。利用这种方法,我们有效地对记录在脑组织中的局部场电位以及GW150914(第一次探测到的引力波事件)进行了量子SSD模拟。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hybrid quantum singular spectrum decomposition for time series analysis
Classical data analysis requires computational efforts that become intractable in the age of Big Data. An essential task in time series analysis is the extraction of physically meaningful information from a noisy time series. One algorithm devised for this very purpose is singular spectrum decomposition (SSD), an adaptive method that allows for the extraction of narrow-banded components from non-stationary and non-linear time series. The main computational bottleneck of this algorithm is the singular value decomposition (SVD). Quantum computing could facilitate a speedup in this domain through superior scaling laws. We propose quantum SSD by assigning the SVD subroutine to a quantum computer. The viability for implementation and performance of this hybrid algorithm on a near term hybrid quantum computer is investigated. In this work, we show that by employing randomized SVD, we can impose a qubit limit on one of the circuits to improve scalibility. Using this, we efficiently perform quantum SSD on simulations of local field potentials recorded in brain tissue, as well as GW150914, the first detected gravitational wave event.
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CiteScore
9.90
自引率
0.00%
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