‪Research Associate @ HWU, Edinburgh‬ - ‪‪133-mal zitiert‬‬ - ‪Quantum Information Theory‬ - ‪Machine Learning‬ - ‪Quantum Mechanics‬ - ‪Quantum Metrology‬

2023年12月28日 · View a PDF of the paper titled Model-aware reinforcement learning for high-performance Bayesian experimental design in quantum metrology, by Federico Belliardo and 3 other authors

Federico Belliardo. Research Associate, School of Engineering & Physical Sciences; Research Associate, School of Engineering & Physical Sciences, Institute of Photonics and Quantum Sciences

2024年3月8日 · In Belliardo et al., arXiv:2312.16985 (2023), we solved this problem in general, by introducing a procedure capable of optimizing a wide range of tasks in quantum metrology and estimation by combining model-aware reinforcement learning with Bayesian inference. We take a model-based approach to the optimisation where the physics describing the ...

2023年3月2日 · Belliardo, F. & Giovannetti, V. Achieving Heisenberg scaling with maximally entangled states: an analytic upper bound for the attainable root-mean-square error. Phys. Rev.

2024年3月15日 · View a PDF of the paper titled Application of machine learning to experimental design in quantum mechanics, by Federico Belliardo and 2 other authors

Federico Belliardo via Scopus - Elsevier Sub-standard quantum limit estimation precision for a wide resources range 2023 Conference on Lasers and Electro-Optics, CLEO 2023

2021年6月18日 · In this paper we introduce a measure of genuine quantum incompatibility in the estimation task of multiple parameters, that has a geometric character and is backed by a clear operational interpretation.

Magnetic and Mechanical Design of the Large Aperture HTS Superconducting Dipoles for the Accelerator Ring of the Muon Collider

2024年6月14日 · In Belliardo et al. [arXiv:2312.16985], we solved this problem in general by introducing a procedure capable of optimizing a wide range of tasks in quantum metrology and estimation by combining model-aware reinforcement learning with Bayesian inference.