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<p>The study of exoplanetary atmospheres traditionally relies on forward models to analytically compute the spectrum of an exoplanet by fine-tuning numerous chemical and physical parameters. However, the high-dimensionality of parameter space often results in a significant computational overhead. In this work, we introduce a novel approach to atmospheric retrieval leveraging on quantum extreme learning machines (QELMs), a quantum version of the classical Extreme Learning Machine (ELM) – a fast machine learning model typically used for regression and classification. Our method combines classical spectral patching and PCA-based dimensionality reduction with a factorized quantum reservoir that provides nonlinear features for the final linear retrieval map. We distinguish the classical PCA filtering used to mitigate noise in the input spectra from the readout-level robustness observed when the reservoir is executed on noisy quantum hardware. We demonstrate the robustness of our approach through a direct implementation on IBM Fez. The proposed QELM architecture highlights the potential of quantum computing in the analysis of astrophysical datasets, retrieving successfully the concentration of <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(CH_4, CO_2, H_2O\)</EquationSource> </InlineEquation> and the radius of over the <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(90\%\)</EquationSource> </InlineEquation> of the dataset in the infinite statistics limit, while remaining robust under realistic noise conditions on IBM Fez, paving the way, in the near future, to faster, more efficient, and more accurate models for the study of exoplanetary atmospheres.</p>

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Exoplanetary atmospheres retrieval via a quantum extreme learning machine

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月22日

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  • Marco Vetrano,
  • Tiziano Zingales,
  • G. Massimo Palma,
  • Salvatore Lorenzo

摘要

The study of exoplanetary atmospheres traditionally relies on forward models to analytically compute the spectrum of an exoplanet by fine-tuning numerous chemical and physical parameters. However, the high-dimensionality of parameter space often results in a significant computational overhead. In this work, we introduce a novel approach to atmospheric retrieval leveraging on quantum extreme learning machines (QELMs), a quantum version of the classical Extreme Learning Machine (ELM) – a fast machine learning model typically used for regression and classification. Our method combines classical spectral patching and PCA-based dimensionality reduction with a factorized quantum reservoir that provides nonlinear features for the final linear retrieval map. We distinguish the classical PCA filtering used to mitigate noise in the input spectra from the readout-level robustness observed when the reservoir is executed on noisy quantum hardware. We demonstrate the robustness of our approach through a direct implementation on IBM Fez. The proposed QELM architecture highlights the potential of quantum computing in the analysis of astrophysical datasets, retrieving successfully the concentration of \(CH_4, CO_2, H_2O\) and the radius of over the \(90\%\) of the dataset in the infinite statistics limit, while remaining robust under realistic noise conditions on IBM Fez, paving the way, in the near future, to faster, more efficient, and more accurate models for the study of exoplanetary atmospheres.