Exoplanetary atmospheres retrieval via a quantum extreme learning machine
This addresses a computational bottleneck for astrophysicists studying exoplanetary atmospheres, but it appears incremental as it adapts quantum techniques to an existing domain.
The paper tackles the computational overhead in exoplanetary atmospheres retrieval by introducing a quantum extreme learning machine (QELM) framework, demonstrating fault tolerance on IBM Fez and showing potential for faster and more accurate models.
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.