Quantum reservoir computing implementation on coherently coupled quantum oscillators
Abstract Quantum reservoir computing is a promising approach for quantum neural networks, capable of solving hard learning tasks on both classical and quantum input data. However, current approaches with qubits suffer from limited connectivity. We propose an implementation for quantum reservoir that...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-07-01
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| Series: | npj Quantum Information |
| Online Access: | https://doi.org/10.1038/s41534-023-00734-4 |
| _version_ | 1797784488414019584 |
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| author | Julien Dudas Baptiste Carles Erwan Plouet Frank Alice Mizrahi Julie Grollier Danijela Marković |
| author_facet | Julien Dudas Baptiste Carles Erwan Plouet Frank Alice Mizrahi Julie Grollier Danijela Marković |
| author_sort | Julien Dudas |
| collection | DOAJ |
| description | Abstract Quantum reservoir computing is a promising approach for quantum neural networks, capable of solving hard learning tasks on both classical and quantum input data. However, current approaches with qubits suffer from limited connectivity. We propose an implementation for quantum reservoir that obtains a large number of densely connected neurons by using parametrically coupled quantum oscillators instead of physically coupled qubits. We analyze a specific hardware implementation based on superconducting circuits: with just two coupled quantum oscillators, we create a quantum reservoir comprising up to 81 neurons. We obtain state-of-the-art accuracy of 99% on benchmark tasks that otherwise require at least 24 classical oscillators to be solved. Our results give the coupling and dissipation requirements in the system and show how they affect the performance of the quantum reservoir. Beyond quantum reservoir computing, the use of parametrically coupled bosonic modes holds promise for realizing large quantum neural network architectures, with billions of neurons implemented with only 10 coupled quantum oscillators. |
| first_indexed | 2024-03-13T00:40:37Z |
| format | Article |
| id | doaj.art-628bf3f49ad84fc5a3e00240c8e2a325 |
| institution | Directory Open Access Journal |
| issn | 2056-6387 |
| language | English |
| last_indexed | 2024-03-13T00:40:37Z |
| publishDate | 2023-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Quantum Information |
| spelling | doaj.art-628bf3f49ad84fc5a3e00240c8e2a3252023-07-09T11:20:29ZengNature Portfolionpj Quantum Information2056-63872023-07-01911710.1038/s41534-023-00734-4Quantum reservoir computing implementation on coherently coupled quantum oscillatorsJulien Dudas0Baptiste Carles1Erwan Plouet2Frank Alice Mizrahi3Julie Grollier4Danijela Marković5Unité Mixte de Physique CNRS, Thales, Université Paris-SaclayUnité Mixte de Physique CNRS, Thales, Université Paris-SaclayUnité Mixte de Physique CNRS, Thales, Université Paris-SaclayUnité Mixte de Physique CNRS, Thales, Université Paris-SaclayUnité Mixte de Physique CNRS, Thales, Université Paris-SaclayUnité Mixte de Physique CNRS, Thales, Université Paris-SaclayAbstract Quantum reservoir computing is a promising approach for quantum neural networks, capable of solving hard learning tasks on both classical and quantum input data. However, current approaches with qubits suffer from limited connectivity. We propose an implementation for quantum reservoir that obtains a large number of densely connected neurons by using parametrically coupled quantum oscillators instead of physically coupled qubits. We analyze a specific hardware implementation based on superconducting circuits: with just two coupled quantum oscillators, we create a quantum reservoir comprising up to 81 neurons. We obtain state-of-the-art accuracy of 99% on benchmark tasks that otherwise require at least 24 classical oscillators to be solved. Our results give the coupling and dissipation requirements in the system and show how they affect the performance of the quantum reservoir. Beyond quantum reservoir computing, the use of parametrically coupled bosonic modes holds promise for realizing large quantum neural network architectures, with billions of neurons implemented with only 10 coupled quantum oscillators.https://doi.org/10.1038/s41534-023-00734-4 |
| spellingShingle | Julien Dudas Baptiste Carles Erwan Plouet Frank Alice Mizrahi Julie Grollier Danijela Marković Quantum reservoir computing implementation on coherently coupled quantum oscillators npj Quantum Information |
| title | Quantum reservoir computing implementation on coherently coupled quantum oscillators |
| title_full | Quantum reservoir computing implementation on coherently coupled quantum oscillators |
| title_fullStr | Quantum reservoir computing implementation on coherently coupled quantum oscillators |
| title_full_unstemmed | Quantum reservoir computing implementation on coherently coupled quantum oscillators |
| title_short | Quantum reservoir computing implementation on coherently coupled quantum oscillators |
| title_sort | quantum reservoir computing implementation on coherently coupled quantum oscillators |
| url | https://doi.org/10.1038/s41534-023-00734-4 |
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