Quantum machine learning with Adaptive Boson Sampling via post-selection
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 computati...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-55877-z |
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| author | 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 Di Giano Marco Gardina Francesco Ceccarelli Giacomo Corrielli Ulysse Chabaud Roberto Osellame Massimiliano Dispenza Fabio Sciarrino |
| author_facet | 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 Di Giano Marco Gardina Francesco Ceccarelli Giacomo Corrielli Ulysse Chabaud Roberto Osellame Massimiliano Dispenza Fabio Sciarrino |
| author_sort | Francesco Hoch |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a96f59696648482abb6242beb048022b |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-a96f59696648482abb6242beb048022b2025-01-26T12:40:48ZengNature PortfolioNature Communications2041-17232025-01-0116111110.1038/s41467-025-55877-zQuantum machine learning with Adaptive Boson Sampling via post-selectionFrancesco Hoch0Eugenio Caruccio1Giovanni Rodari2Tommaso Francalanci3Alessia Suprano4Taira Giordani5Gonzalo Carvacho6Nicolò Spagnolo7Seid Koudia8Massimiliano Proietti9Carlo Liorni10Filippo Cerocchi11Riccardo Albiero12Niki Di Giano13Marco Gardina14Francesco Ceccarelli15Giacomo Corrielli16Ulysse Chabaud17Roberto Osellame18Massimiliano Dispenza19Fabio Sciarrino20Dipartimento di Fisica, Sapienza Università di RomaDipartimento di Fisica, Sapienza Università di RomaDipartimento di Fisica, Sapienza Università di RomaDipartimento di Fisica, Sapienza Università di RomaDipartimento di Fisica, Sapienza Università di RomaDipartimento di Fisica, Sapienza Università di RomaDipartimento di Fisica, Sapienza Università di RomaDipartimento di Fisica, Sapienza Università di RomaLeonardo S.p.A., Leonardo Labs, Quantum technologies labLeonardo S.p.A., Leonardo Labs, Quantum technologies labLeonardo S.p.A., Leonardo Labs, Quantum technologies labLeonardo S.p.A., Cyber & Security Solutions DivisionDipartimento di Fisica, Politecnico di MilanoDipartimento di Fisica, Politecnico di MilanoIstituto di Fotonica e Nanotecnologie, Consiglio Nazionale delle Ricerche (IFN-CNR)Istituto di Fotonica e Nanotecnologie, Consiglio Nazionale delle Ricerche (IFN-CNR)Istituto di Fotonica e Nanotecnologie, Consiglio Nazionale delle Ricerche (IFN-CNR)DIENS, École Normale Supérieure, PSL University, CNRS, INRIAIstituto di Fotonica e Nanotecnologie, Consiglio Nazionale delle Ricerche (IFN-CNR)Leonardo S.p.A., Leonardo Labs, Quantum technologies labDipartimento di Fisica, Sapienza Università di RomaAbstract 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.https://doi.org/10.1038/s41467-025-55877-z |
| spellingShingle | 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 Di Giano Marco Gardina Francesco Ceccarelli Giacomo Corrielli Ulysse Chabaud Roberto Osellame Massimiliano Dispenza Fabio Sciarrino Quantum machine learning with Adaptive Boson Sampling via post-selection Nature Communications |
| title | Quantum machine learning with Adaptive Boson Sampling via post-selection |
| title_full | Quantum machine learning with Adaptive Boson Sampling via post-selection |
| title_fullStr | Quantum machine learning with Adaptive Boson Sampling via post-selection |
| title_full_unstemmed | Quantum machine learning with Adaptive Boson Sampling via post-selection |
| title_short | Quantum machine learning with Adaptive Boson Sampling via post-selection |
| title_sort | quantum machine learning with adaptive boson sampling via post selection |
| url | https://doi.org/10.1038/s41467-025-55877-z |
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