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Complexity-powered machine intelligent classification of quantum many-body dynamics
Zhaoran Feng1, Jiangzhi Chen1, Ce Wang1
1Tongji University, Center for Phononics and Thermal Energy Science, China-EU Joint Laboratory on Nanophononics, Shanghai Key Laboratory of Special Artificial Microstructure Materials and Technology, School of Physics Science and Engineering, Shanghai 200092, China.
Physical Review. E
|July 24, 2026
Summary
This study introduces a data-driven method using amplified temporal fluctuations to classify quantum many-body dynamics. It offers a novel approach for identifying complex quantum phases without prior physics knowledge.
Area of Science:
- Quantum physics
- Data science
- Machine learning
Background:
- Classifying quantum phases from time series data is challenging and requires expert knowledge.
- Existing methods lack detail and face significant hurdles in analyzing many-body dynamics.
Purpose of the Study:
- To develop a purely data-driven machine intelligence classification for quantum many-body dynamics.
- To introduce a novel distance measure that captures temporal fluctuation complexity.
Main Methods:
- Introduced a temporal fluctuation-amplified distance measure.
- Applied unsupervised manifold learning to dynamic evolution series.
- Tested on discrete time crystal (DTC) and Aubry-André (AA) models.
Main Results:
- Achieved remarkable improvements in unsupervised manifold learning of quantum many-body dynamics.
- Demonstrated effectiveness in imperfect, disordered, and noisy conditions.
- Successfully classified dynamic phases in complex systems.
Conclusions:
- The developed method provides a powerful, data-driven tool for quantum phase classification.
- This approach bypasses the need for prior physics expertise.
- Potential applications include disaster prediction and financial trend analysis.
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