Quantum reservoir computing implementation on coherently coupled quantum oscillators
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...
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| Published in: | npj quantum information Vol. 9; no. 1; pp. 64 - 7 |
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| Format: | Journal Article |
| Language: | English |
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Nature Publishing Group UK
07.07.2023
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| ISSN: | 2056-6387, 2056-6387 |
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| 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. |
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| AbstractList | 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. 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. |
| ArticleNumber | 64 |
| Author | Grollier, Julie Dudas, Julien Mizrahi, Frank Alice Marković, Danijela Plouet, Erwan Carles, Baptiste |
| Author_xml | – sequence: 1 givenname: Julien surname: Dudas fullname: Dudas, Julien organization: Unité Mixte de Physique CNRS, Thales, Université Paris-Saclay – sequence: 2 givenname: Baptiste orcidid: 0000-0002-8292-6565 surname: Carles fullname: Carles, Baptiste organization: Unité Mixte de Physique CNRS, Thales, Université Paris-Saclay – sequence: 3 givenname: Erwan orcidid: 0000-0001-6401-8064 surname: Plouet fullname: Plouet, Erwan organization: Unité Mixte de Physique CNRS, Thales, Université Paris-Saclay – sequence: 4 givenname: Frank Alice orcidid: 0000-0003-2043-049X surname: Mizrahi fullname: Mizrahi, Frank Alice organization: Unité Mixte de Physique CNRS, Thales, Université Paris-Saclay – sequence: 5 givenname: Julie surname: Grollier fullname: Grollier, Julie organization: Unité Mixte de Physique CNRS, Thales, Université Paris-Saclay – sequence: 6 givenname: Danijela orcidid: 0000-0001-7521-217X surname: Marković fullname: Marković, Danijela email: danijela.markovic@cnrs-thales.fr organization: Unité Mixte de Physique CNRS, Thales, Université Paris-Saclay |
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| Title | Quantum reservoir computing implementation on coherently coupled quantum oscillators |
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