Effects of dissipation in reservoir computing using spin qubit array

Abstract Reservoir computing (RC) is one of the hottest topics as a promising application for many physical devices because their device characteristics can be directly used in computing sequences. Quantum RC is also a promising candidate for the application of even small-number qubit systems. Here, we propose a quantum RC based on the spin qubit system reflecting the status of the spin qubits in experiments of a one-dimensional qubit array. Spin qubits are coupled via the Heisenberg interaction, and data sequences are inputted to one of the spin qubits by pulsed rotations. By introducing dissipation, we obtain satisfactory performance as a quantum RC.

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Publication Details

Journal
Japanese Journal of Applied Physics
Published
2025-03-12
DOI
https://doi.org/10.35848/1347-4065/adbf9e
Citations
3
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
7.44
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article

Effects of dissipation in reservoir computing using spin qubit array

Tetsufumi Tanamoto, Taro Kanao, Shion Mifune
3 citations
Japanese Journal of Applied Physics
Neural Networks and Reservoir Computing
7.44
article

Effects of dissipation in reservoir computing using spin qubit array

Tetsufumi Tanamoto, Taro Kanao, Shion Mifune
article en
3 citations

Abstract

Abstract Reservoir computing (RC) is one of the hottest topics as a promising application for many physical devices because their device characteristics can be directly used in computing sequences. Quantum RC is also a promising candidate for the application of even small-number qubit systems. Here, we propose a quantum RC based on the spin qubit system reflecting the status of the spin qubits in experiments of a one-dimensional qubit array. Spin qubits are coupled via the Heisenberg interaction, and data sequences are inputted to one of the spin qubits by pulsed rotations. By introducing dissipation, we obtain satisfactory performance as a quantum RC.

Japanese Journal of Applied PhysicsVol. 64(4)
Shibaura Institute of Technology (JP), Teikyo University (JP)
Openalex Percentile: Top 4%
Neural Networks and Reservoir Computing
7.44
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