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Design of molecular lanthanide-based integrated quantum light sources

Silvia Bonabello, Rodolpho Alessandro, Nesta Silva, Dimitrije Mara, Fabio Travagin, Marzia Petreti, Marco Saccone, Matteo Melegari, Luciano Marchiò, Luca Pilia, Giovanni Battista Giovenzana, Flavia Artizzu

Published Jan 29, 2026• 0 citations

Abstract

Funding: This work received support from the European Innovation Council under the Horizon Europe framework programme through a HORIZON-EIC-2022-PATHFINDERCHALLENGES-01-06 project "ARTEMIS-Molecular Materials for on-chip integrated quantum light sources"-GA n. 101115149. We also acknowledge financial support under the National Recovery and Resilience Plan (NRRP), Mission 4, Component 2, Investment 1.1, Call for tender No. 104 published on 2.2.2022 by the Italian Ministry of University and Research (MUR), funded by the European Union-Next Generation EU-Project Title "QuantaMol-Molecular Quantum Light Sources"-CUP C53D23007330001-Grant Assignment Decree No. 1380 adopted on 01-09-2023 by the Italian Ministry of University and Research (MUR).

DOI: 10.26434/chemrxiv.10001687/v1

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Funding: This work received support from the European Innovation Council under the Horizon Europe framework programme through a HORIZON-EIC-2022-PATHFINDERCHALLENGES-01-06 project "ARTEMIS-Molecular Materials for on-chip integrated quantum light sources"-GA n. 101115149. We also acknowledge financial support under the National Recovery and Resilience Plan (NRRP), Mission 4, Component 2, Investment 1.1, Call for tender No. 104 published on 2.2.2022 by the Italian Ministry of University and Research (MUR), funded by the European Union-Next Generation EU-Project Title "QuantaMol-Molecular Quantum Light Sources"-CUP C53D23007330001-Grant Assignment Decree No. 1380 adopted on 01-09-2023 by the Italian Ministry of University and Research (MUR).

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Acknowledgements: AB acknowledges financial support from the European Research Council (ERC Grant Agreement No. 81876) and VILLUM FONDEN via the QMATH Centre of Excellence (Grant No.10059). Moreover, AB is supported by the French National Research Agency in the framework of the “France 2030” program (ANR-11-LABX-0025-01) for the LabEx PERSYVAL. AC acknowledges the support of the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) â€“ Project-ID 470903074 â€“ TRR 352. APH is partially supported by the Escuela Técnica Superior de Ingenieros Industriales (UNED) of Spain, project 2023-ETSII-UNED-01, as well as by the Spanish Ministerio de Ciencia e Innovación project PID2020-113523GB-I00 and by Comunidad de Madrid project QUITEMAD-CM P2018/TCS4342. AC and AB are grateful for the hospitality of Perimeter Institute where part of this work was carried out. Research at Perimeter Institute is supported in part by the Government of Canada through the Department of Innovation, Science and Economic Development and by the Province of Ontario through the Ministry of Colleges and Universities. This research was also supported in part by the Simons Foundation through the Simons Foundation Emmy Noether Fellows Program at Perimeter Institute.

The promise of quantum computation to achieve a speedup over classical computation led to a surge of interest in exploring new quantum algorithms for data analysis problems.Feature Selection, a technique that selects the most relevant features from a dataset, is a critical step in data analysis.With several Quantum Feature Selection techniques proposed in the literature, this study exhibits the potential of quantum algorithms to enhance Feature Selection and other tasks that leverage the variance.This study proposes a novel quantum algorithm for estimating the variance over a set of real data.Importantly, after state preparation, the algorithm's complexity exhibits logarithmic characteristics in both its width and depth.The quantum algorithm applies to the Feature Selection problem by designing a Hybrid Quantum Feature Selection (HQFS) algorithm.This work showcases an implementation of HQFS and assesses it on two synthetic datasets and a real dataset.* This study was carried out within the National Centre on HPC, Big Data and Quantum Computing -SPOKE 10 (Quantum Computing) and received funding from the European Union Next-GenerationEU -National Recovery and Resilience Plan (NRRP) -MISSION 4, COMPONENT 2 -CUP N. I53C22000690001.This manuscript reflects only the authors' views and opinions, neither the European Union nor the European Commission can be considered responsible for them.