Exoplanetary atmospheres retrieval via a quantum extreme learning machine

• URL: http://arxiv.org/abs/2509.03617v1
• Date: Wed, 03 Sep 2025 18:10:07 GMT
• Title: Exoplanetary atmospheres retrieval via a quantum extreme learning machine
• Authors: Marco Vetrano, Tiziano Zingales, G. Massimo Palma, Salvatore Lorenzo,
• Abstract summary: We introduce a novel approach to atmospheric retrieval leveraging on quantum extreme learning machines (QELMs)<n>QELMs are quantum machine learning techniques that employ quantum systems as a black box for processing input data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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). QELMs are quantum machine learning techniques that employ quantum systems as a black box for processing input data. In this work, we propose a framework for extracting exoplanetary atmospheric features using QELMs, employing an intrinsically fault-tolerant strategy suitable for near-term quantum devices, and we demonstrate such fault tolerance with a direct implementation on IBM Fez. The QELM architecture we present shows the potential of quantum computing in the analysis of astrophysical datasets and may, in the near-term future, unlock new computational tools to implement fast, efficient, and more accurate models in the study of exoplanetary atmospheres.

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