Enregistré dans:
| Auteurs principaux: | , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2512.14169 |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866915678819188736 |
|---|---|
| author | Kafashi, Parsa Orujlu, Mozhgan |
| author_facet | Kafashi, Parsa Orujlu, Mozhgan |
| contents | We present a quantum information-inspired framework for analyzing complex systems through multivariate time series. In this approach the system's state is encoded into a density matrix, providing a compact representation of higher-order correlations and dependencies. This formulation enables precise quantification of the relative influence among time series, tracking of their response to external perturbations and also the definition of a recovery timescale without need for dimensional reduction. By leveraging tools such as fidelity from quantum information theory, our method naturally captures higher-order co-fluctuations beyond pairwise statistics, offering a holistic characterization of resilience and similarity in high-dimensional dynamics. We validate this approach on synthetic data generated by a 9-dimensional modified Lorenz-96 model and demonstrate its utility on real-world climate data, analyzing global temperature anomalies across nine regions, quantifying the dissimilarity of each 288-month time window up to July 2025 relative to the 1850-1874 baseline period. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_14169 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum-Inspired Approach to Analyzing Complex System Dynamics Kafashi, Parsa Orujlu, Mozhgan Chaotic Dynamics Quantum Physics We present a quantum information-inspired framework for analyzing complex systems through multivariate time series. In this approach the system's state is encoded into a density matrix, providing a compact representation of higher-order correlations and dependencies. This formulation enables precise quantification of the relative influence among time series, tracking of their response to external perturbations and also the definition of a recovery timescale without need for dimensional reduction. By leveraging tools such as fidelity from quantum information theory, our method naturally captures higher-order co-fluctuations beyond pairwise statistics, offering a holistic characterization of resilience and similarity in high-dimensional dynamics. We validate this approach on synthetic data generated by a 9-dimensional modified Lorenz-96 model and demonstrate its utility on real-world climate data, analyzing global temperature anomalies across nine regions, quantifying the dissimilarity of each 288-month time window up to July 2025 relative to the 1850-1874 baseline period. |
| title | Quantum-Inspired Approach to Analyzing Complex System Dynamics |
| topic | Chaotic Dynamics Quantum Physics |
| url | https://arxiv.org/abs/2512.14169 |