Embedded Files
Image from Sci. Technol. 6 (2025) 015056
Our research focuses on harnessing the potential of quantum computing for algorithmic applications, particularly in areas where a quantum advantage is established or within reach. We concentrate on the NISQ (Noisy Intermediate-Scale Quantum) regime, aiming to bridge theoretical models with practical implementations.
Ongoing research directions include:
Quantum simulation, with a strong focus on chemistry and materials science problems;
Quantum machine learning, especially in physics-informed and data-driven applications;
Quantum Error Correction and Mitigation for enhancing computational fidelity.
While much of our work is theoretical, we actively test and benchmark quantum algorithms on real hardware, using IBM Quantum devices as well as our own in-house quantum computing infrastructure. This integrated approach helps us close the gap between theory and experiment, paving the way for real-world quantum applications.
Contact: Andrea Giachero
Selected Bibliography
Selected Bibliography
Local fermion-to-qudit mappings, R. Carobene, S. Barison, A. Giachero, and J. Nys, Phys. Rev. A 112, 032619, Sep. 2025, DOI: doi.org/10.1103/bcs4-hxl3, arXiv:2412.05616 [quant-ph]
Enhanced feature encoding and classification on distributed quantum hardware, R. Moretti, A. Giachero, V. Radescu, M. Grossi, Mach. Learn.: Sci. Technol. 6 (2025) 015056 , DOI: dx.doi.org/10.1088/2632-2153/adb4bc, arXiv:2412.01664 [quant-ph]
Assessment of few-hits machine learning classification algorithms for low energy physics in liquid argon detectors, M. Biassoni et al., Eur. Phys. J. Plus 139 (2024) 8, 723 , DOI: doi.org/10.1140/epjp/s13360-024-05287-9, arXiv:2305.09744 [physics.ins-det]
Sequence of penalties method to study excited states using VQE, R. Carobene, S. Barison, A. Giachero, Quantum Sci. Technol. 8 (2023) 035014, , DOI: doi.org/10.1088/2058-9565/acd1a9, arXiv:2304.05262 [quant-ph]
Page updated
Report abuse