Hybrid quantum-classical algorithm accelerates dynamic mode decomposition for high-dimensional time series analysis

Seeking to reduce the computing power needed for the widely used dynamic mode decomposition algorithm, a team of researchers in China led by Guo-Ping Guo developed a quantum-classical hybrid algorithm. They tested their algorithm in three application scenarios: data denoising, scene background extraction, and fluid dynamics analysis. They determined that it can operate with only a small number of samples and has a quantum advantage in the analysis of high-dimensional time series.

algorithm analysis application china computer sciences computing computing power data denoising dynamic dynamics extraction fluid dynamics hybrid power quantum reduce researchers series team time series