Quantum machine learning with Adaptive Boson Sampling via post-selection

Francesco Hoch, Eugenio Caruccio, Giovanni Rodari, Tommaso Francalanci, Alessia Suprano, Taira Giordani, Gonzalo Carvacho, Nicolò Spagnolo, Seid Koudia, Massimiliano Proietti, Carlo Liorni, Filippo Cerocchi, Riccardo Albiero, Niki Giano, Marco Gardina, Francesco Ceccarelli, Giacomo Corrielli, Ulysse Chabaud, Roberto Osellame, Massimiliano Dispenza and Fabio Sciarrino ()
Additional contact information
Francesco Hoch: Sapienza Università di Roma
Eugenio Caruccio: Sapienza Università di Roma
Giovanni Rodari: Sapienza Università di Roma
Tommaso Francalanci: Sapienza Università di Roma
Alessia Suprano: Sapienza Università di Roma
Taira Giordani: Sapienza Università di Roma
Gonzalo Carvacho: Sapienza Università di Roma
Nicolò Spagnolo: Sapienza Università di Roma
Seid Koudia: Quantum technologies lab
Massimiliano Proietti: Quantum technologies lab
Carlo Liorni: Quantum technologies lab
Filippo Cerocchi: Cyber & Security Solutions Division
Riccardo Albiero: Politecnico di Milano
Niki Giano: Politecnico di Milano
Marco Gardina: Consiglio Nazionale delle Ricerche (IFN-CNR)
Francesco Ceccarelli: Consiglio Nazionale delle Ricerche (IFN-CNR)
Giacomo Corrielli: Consiglio Nazionale delle Ricerche (IFN-CNR)
Ulysse Chabaud: INRIA
Roberto Osellame: Consiglio Nazionale delle Ricerche (IFN-CNR)
Massimiliano Dispenza: Quantum technologies lab
Fabio Sciarrino: Sapienza Università di Roma

Nature Communications, 2025, vol. 16, issue 1, 1-11

Abstract: Abstract The implementation of large-scale universal quantum computation represents a challenging and ambitious task on the road to quantum processing of information. In recent years, an intermediate approach has been pursued to demonstrate quantum computational advantage via non-universal computational models. A relevant example for photonic platforms has been provided by the Boson Sampling paradigm and its variants, which are known to be computationally hard while requiring at the same time only the manipulation of the generated photonic resources via linear optics and detection. Beside quantum computational advantage demonstrations, a promising direction towards possibly useful applications can be found in the field of quantum machine learning, considering the currently almost unexplored intermediate scenario between non-adaptive linear optics and universal photonic quantum computation. Here, we report the experimental implementation of quantum machine learning protocols by adding adaptivity via post-selection to a Boson Sampling platform based on universal programmable photonic circuits fabricated via femtosecond laser writing. Our experimental results demonstrate that Adaptive Boson Sampling is a viable route towards dimension-enhanced quantum machine learning with linear optical devices.

Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-025-55877-z Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-55877-z

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-025-55877-z

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().