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Quantum Recurrence Plot Algorithm Based on Quantum Principal Component Analysis
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AI
DOI：10.1002/qute.70357.png)
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Abstract
En 中文
Recurrence Plot (RP) is a method employed to analyze the periodicity, chaoticity, and nonlinear characteristics of complex systems. Quantum Principal Component Analysis (QPCA), on the other hand, achieves dimensionality reduction of sample data using density matrices based on quantum circuits. We improve the distance threshold function of the recurrence plot algorithm using a density operator conceptually equivalent to the covariance matrix, integrate it with quantum circuits, and thereby develop a Quantum Recurrence Plot (QRP) algorithm. This algorithm achieves ultra-high efficiency in parallel computing, reduces computational costs, and simultaneously upgrades the traditional grayscale recurrence plot to colored heatmaps, enabling a better revelation of the system's dynamical characteristics.
Keywords:
principal component analysis
quantum principal component analysis
quantum recurrence plot
recurrence plot
Journal
A
IF:
4.3
Papers:
409
Citations:
3.2K
