Quantum Theory Group in Palermo

We are a large team, based in the Department of Physics and Chemistry at University of Palermo, exploring the theory of quantum systems and processes.

We address frontier questions in the engineering, control, characterisation and exploitations of quantum states and resources. The expertise of the members of our group spans a large range of topics, from Quantum Optics to Condensed Matter and Statistical Physics, from Quantum Information Processing to Open System Dynamics and Artificial Intelligence. We also enjoy exploring the intricacies of the foundations of quantum mechanics from an information theoretic standpoint. Image

A key aim of our research is the development of theoretical frameworks of prompt experimental translation to understand the interplay between quantum resources, non-equilibrium physics, and control.

While pursuing these goals, we interact with some of the leading experimental teams addressing photonics, optomechanics, cold atom, and semiconductor-based platforms. Get in touch with us if you are interested in our research and to explore potentials for collaborations!

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Papers, Projetcs and …

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 combines classical spectral patching and PCA-based dimensionality reduction with a factorized quantum reservoir that provides nonlinear features for the final linear retrieval map. We distinguish the classical PCA filtering used to mitigate noise in the input spectra from the readout-level robustness observed when the reservoir is executed on noisy quantum hardware. 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 $CH_4$, $CO_2$, and $H_2O$, and the radius of over the $90$% of the dataset in the infinite statistics limit, while remaining robust under realistic noise conditions on IBM Fez, paving the way, in the near future, to faster, more efficient, and more accurate models for the study of exoplanetary atmospheres.