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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Podrobná bibliografie
Vydáno v:	npj quantum information Ročník 9; číslo 1; s. 64 - 7
Hlavní autoři:	Dudas, Julien, Carles, Baptiste, Plouet, Erwan, Mizrahi, Frank Alice, Grollier, Julie, Marković, Danijela
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Nature Publishing Group UK 07.07.2023 Nature Publishing Group Nature Nature Partner Journals Nature Portfolio
Témata:	639/766/119/1003 639/766/259 639/766/483/481 Classical and Quantum Gravitation Information theory and computation MATHEMATICS AND COMPUTING Neural networks Oscillators Physics Physics and Astronomy Quantum Computing Quantum Field Theories Quantum information Quantum Information Technology Quantum Physics Relativity Theory Spintronics String Theory Superconducting properties and materials
ISSN:	2056-6387, 2056-6387
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Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 USDOE Office of Science (SC), Basic Energy Sciences (BES) SC0019273; 101076898 European Research Council (ERC)
ISSN:	2056-6387 2056-6387
DOI:	10.1038/s41534-023-00734-4