Una selezione di pubblicazioni, presentazioni e contributi di ricerca dei Leonardo Innovation Labs, che sviluppano tecnologie avanzate – dall'intelligenza artificiale al quantum computing, dai digital twin ai sistemi autonomi – per supportare l'innovazione applicata e il trasferimento tecnologico nei settori chiave del Gruppo.
Quantum technologies Lab
- S. Corli, et al. “Benchmarking the emulation of measurement-based quantum computing through the Max K-Cut algorithm”, accepted in Quantum Information Processing, 2025.
- F. Hoch., et al. "Quantum machine learning with Adaptive Boson Sampling via post-selection," in Nature Communications, vol. 16, no. 1, pp. 902, 2025.
- L. Mancini, et al. "Atto-Watt Photo-Detection at Mid-Infrared Wavelengths by a Room-Temperature Balanced Heterodyne Set-Up," in Laser & Photonics Reviews, pp. e01339, 2025.
- C. Pitsch, et al. "Toward video-rate quantum ghost imaging," in APL Photonics, vol. 10, no. 10, pp. 100801, 2025.
- A. Suprano, et al., "Quantum Ghost Imaging of remote targets with novel SPAD technology," in Conference on Lasers and Electro-Optics/Europe (CLEO/Europe 2025) and European Quantum Electronics Conference (EQEC 2025), 2025.
- M. Vischi, et al. "Simulating photonic devices with noisy optical elements," in Phys. Rev. Res., vol. 6, pp. 033337, 2024.
- F. Levi, et al., "Improving twin-field QKD with optical clock technologies," in CLEO 2024, 2024.
- G. Bertaina, et al. "Phase Noise in Real-World Twin-Field Quantum Key Distribution," in Advanced Quantum Technologies, vol. 7, no. 6, pp. 2400032, 2024.
- S. Corli, et al, "A Max K-Cut Implementation for QAOA in the Measurement Based Quantum Computing Formalism," in 2023 IEEE International Conference on Quantum Computing and Engineering (QCE), 2023, pp. 284-285.
- M. Proietti, F. Cerocchi, M. Dispenza. "Native measurement-based quantum approximate optimization algorithm applied to the Max $K$-Cut problem," in Phys. Rev. A, vol. 106, pp. 022437, 2022.