A recurrent Gaussian quantum network for online processing of quantum time series.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
29 May 2024
29 May 2024
Historique:
received:
13
11
2023
accepted:
30
04
2024
medline:
30
5
2024
pubmed:
30
5
2024
entrez:
29
5
2024
Statut:
epublish
Résumé
Over the last decade, researchers have studied the interplay between quantum computing and classical machine learning algorithms. However, measurements often disturb or destroy quantum states, requiring multiple repetitions of data processing to estimate observable values. In particular, this prevents online (real-time, single-shot) processing of temporal data as measurements are commonly performed during intermediate stages. Recently, it was proposed to sidestep this issue by focusing on tasks with quantum output, eliminating the need for detectors. Inspired by reservoir computers, a model was proposed where only a subset of the internal parameters are trained while keeping the others fixed at random values. Here, we also process quantum time series, but we do so using a Recurrent Gaussian Quantum Network (RGQN) of which all internal interactions can be trained. As expected, this increased flexibility yields higher performance in benchmark tasks. Building on this, we show that the RGQN can tackle two quantum communication tasks, while also removing some hardware restrictions of the currently available methods. First, our approach is more resource efficient to enhance the transmission rate of quantum channels that experience certain memory effects. Second, it can counteract similar memory effects if they are unwanted, a task that could previously only be solved when redundantly encoded input signals could be provided. Finally, we run a small-scale version of the last task on Xanadu's photonic processor Borealis.
Identifiants
pubmed: 38811683
doi: 10.1038/s41598-024-61004-7
pii: 10.1038/s41598-024-61004-7
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
12322Subventions
Organisme : Fonds Wetenschappelijk Onderzoek
ID : G006020N
Organisme : Fonds Wetenschappelijk Onderzoek
ID : G006020N
Organisme : Fonds Wetenschappelijk Onderzoek
ID : G006020N
Organisme : HORIZON EUROPE Framework Programme
ID : 101070195
Organisme : HORIZON EUROPE Framework Programme
ID : 101070195
Organisme : HORIZON EUROPE Framework Programme
ID : 101070195
Organisme : EOS - The Excellence Of Science
ID : 40007536
Organisme : EOS - The Excellence Of Science
ID : 40007536
Organisme : EOS - The Excellence Of Science
ID : 40007536
Informations de copyright
© 2024. The Author(s).
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