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<p>We present a novel weather analysis of San Francisco from 2009 to 2019 using concepts from quantum information theory. We describe how to transform a classical weather dataset into a quantum-inspired analytical framework by treating it as a quantum information system embedded in a quantum-like representation, with data encoded as density matrices in Hilbert space. Transforming classical weather data into quantum states is a mathematical abstraction, whereas a quantum-inspired system is not a quantum-physical system. We study a quantum-inspired analytical framework using quantum information-theoretic metrics, including fidelity, classical information, quantum information, decoherent information, Shannon and von Neumann entropies, and the quantum stability index, to represent and analyze the dataset. Furthermore, we give predictions for each quantum information metric for 2020 and 2021. We fit Fourier-series models to the metrics to explore temporal patterns and structural properties in the data.</p>

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Quantum informative analysis for weather of San Francisco

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月26日

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  • Yasser Kotb,
  • Amr A. Youssef,
  • Dieaa I. Nassr,
  • Hatem M. Bahig

摘要

We present a novel weather analysis of San Francisco from 2009 to 2019 using concepts from quantum information theory. We describe how to transform a classical weather dataset into a quantum-inspired analytical framework by treating it as a quantum information system embedded in a quantum-like representation, with data encoded as density matrices in Hilbert space. Transforming classical weather data into quantum states is a mathematical abstraction, whereas a quantum-inspired system is not a quantum-physical system. We study a quantum-inspired analytical framework using quantum information-theoretic metrics, including fidelity, classical information, quantum information, decoherent information, Shannon and von Neumann entropies, and the quantum stability index, to represent and analyze the dataset. Furthermore, we give predictions for each quantum information metric for 2020 and 2021. We fit Fourier-series models to the metrics to explore temporal patterns and structural properties in the data.