Exoplanetary Atmospheres Retrieval Via A Quantum Extreme Learning Machine

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.

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

Subjects: Quantum Physics (quant-ph); Earth and Planetary Astrophysics (astro-ph.EP); Instrumentation and Methods for Astrophysics (astro-ph.IM); Machine Learning (cs.LG)
Cite as: arXiv:2509.03617 [quant-ph] (or arXiv:2509.03617v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2509.03617
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Submission history
From: Marco Vetrano
[v1] Wed, 3 Sep 2025 18:10:07 UTC (583 KB)
https://arxiv.org/abs/2509.03617
Astrobiology, Exoplanet,