A pictorial representation of how QELM of classical data works. Classical data is encoded in a physical system that collects a series of measurements. The results undergo linear post-processing that maps the reservoir results to the task output. – Astro-Ph.im
Traditionally, studies of deplanetary atmospheres rely on forward models to analytically calculate deplanetal spectra by fine-tuning a number of chemical and physical parameters.
However, the high dimensionality of the parameter space often results in greater computational overhead. This work introduces a new approach to atmospheric recovery leverage in Quantum Extreme Learning Machines (QELMS). QELMS is a quantum machine learning technology that uses quantum systems as a black box for processing input data.
In this work, we propose a framework for extracting exoplanet air features using QELMS, employing an inherently negligent tolerance strategy suitable for short-term quantum devices, demonstrating such fault tolerance in direct implementations in IBM FEZ.
The QELM architecture we present demonstrates the potential of quantum computing in the analysis of astrophysical datasets, allowing for the short-term future to unlock new computational tools for implementing fast, efficient and accurate models in the study of deployment atmospheres.
Marco Vetrano, Tiziano Zingales, G. Massimo Palma, Salvatore Lorenzo
Subjects: Quantum Physics (Quant-PH); Earth and Planetary Astrophysics (Astro-Ph.ep); Astrophysics Instruments and Methods (Astro-PH.IM); Machine Learning (cs.lg)
Quote: 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] Wednesday, September 3, 2025, 18:10:07 UTC (583 kb)
https://arxiv.org/abs/2509.03617
Astrology, exoplanet,
