Effects of dissipation in reservoir computing using a spin qubit array
Shion Mifune, Taro Kanao, and Tetsufumi Tanamoto

TL;DR
This paper explores how dissipation affects the performance of quantum reservoir computing using a one-dimensional spin qubit array with Heisenberg interactions, demonstrating improved results through dissipation.
Contribution
It introduces a quantum reservoir computing model based on a spin qubit array and investigates the impact of dissipation on its performance.
Findings
Dissipation improves quantum RC performance.
Spin qubits coupled via Heisenberg interaction.
Effective data processing with dissipative quantum RC.
Abstract
Reservoir computing (RC) is one of the hottest research topic as an application of many physical devices because the device characteristics can be used directly in computing sequences. Quantum RC is also a promising candidate for application in small-number qubit systems. Here, we propose a quantum RC based on the spin qubit system that reflects the status of the spin qubits in experiments comprising a one-dimensional qubit array. Spin qubits are coupled via the Heisenberg interaction, and data sequences are inputted to one of the spin qubits via pulsed rotations. By introducing dissipation, we obtained a relatively good performance in the quantum RC.
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Taxonomy
TopicsNeural Networks and Reservoir Computing · Neural Networks and Applications · Advanced Memory and Neural Computing
