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), a quantum version of the classical Extreme Learning Machine (ELM) — a fast machine learning model typically used for regression and classification. Our method leverages this framework to extract key atmospheric features while employing a noise-resilient strategy tailored to mitigate hardware noise in near-term quantum devices. 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 CH4, CO2, H2O and the radius of over the 90% of the dataset in the infinite statistics limit, while remaining robust under realistic noise conditions on BM Fez, paving the way, in the near future, to faster, more efficient, and more accurate models for the study of exoplanetary atmospheres.