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researcher

M. Barbieri

107 papers hereh-index 448.6k citations192 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • sole author2
  • first author8
  • middle author60
  • last author35

Across the 105 of 107 papers where every author was matched, so the position is known.

fields
  • quant-ph100
  • physics.optics4
  • cond-mat.mes-hall1
  • cond-mat.quant-gas1
  • physics.ins-det1
same name
  • M. Barbieri — 62 papers, h 32
  • M. Barbieri — 16 papers
  • M. Barbieri — 11 papers, h 4
  • M. Barbieri — 10 papers, h 7
  • M. Barbieri — 8 papers, h 10
  • M. Barbieri — 6 papers, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20032026
most citedExperimental quantum computing without entanglement

885 citations · 7.7k across the 102 of their papers we have counts for

collaborators
Showing 2021 · quant-phShow all

4 papers · 2 filters

quant-ph2021★ 4 cited

Semiparametric estimation in Hong-Ou-Mandel interferometry

Valeria Cimini, Francesco Albarelli, Ilaria Gianani +1

We apply the theory of semiparametric estimation to a Hong-Ou-Mandel interference experiment with a spectrally entangled two-photon state generated by spontaneous parametric downco…

quant-ph2021

Kramers-Kronig relations and precision limits in quantum phase estimation

Ilaria Gianani, Marco Barbieri, Francesco Albarelli +3

The ultimate precision in any measurement is dictated by the physical process implementing the observation. The methods of quantum metrology have now succeeded in establishing boun…

quant-ph2021

Witnessing quantum steering by means of the Fisher information

Ilaria Gianani, Vincenzo Berardi, Marco Barbieri

Capturing specific kinds of quantum correlation is of paramount importance for quantum networking. Different routes can be taken to achieve this task, highlighting different novel…

quant-ph2021★ 1 cited

Experimental Quantum Embedding for Machine Learning

Ilaria Gianani, Ivana Mastroserio, Lorenzo Buffoni +6

The classification of big data usually requires a mapping onto new data clusters which can then be processed by machine learning algorithms by means of more efficient and feasible…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.