{"ID":2875133,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.03617","arxiv_id":"2509.03617","title":"Exoplanetary atmospheres retrieval via a quantum extreme learning machine","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.","short_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 introduc...","url_abs":"https://arxiv.org/abs/2509.03617","url_pdf":"https://arxiv.org/pdf/2509.03617v1","authors":"[\"Marco Vetrano\",\"Tiziano Zingales\",\"G. Massimo Palma\",\"Salvatore Lorenzo\"]","published":"2025-09-03T18:10:07Z","proceeding":"quant-ph","tasks":"[\"quant-ph\",\"astro-ph.EP\",\"astro-ph.IM\",\"cs.LG\"]","methods":"[]","has_code":false}