The Proceedings of Conference of Kanto Branch

Utilizing natural physical reservoir realized on noisy superconducting quantum processors for temporal information processing

Yudai Suzuki, Qi Gao, Kenji Yasuoka, Naoki Yamamoto

Published Jan 1, 2021• 0 citations

Abstract

Quantum reservoir computing is a type of machine-learning scheme that exploits the nonlinearity of natural quantum dynamics for temporal information processing. In a recent study of quantum reservoir computing, an artificially-engineered reservoir is introduced to model the system, which is implemented on noisy gate-base quantum computer to test its performance. However, the proposed system has difficulty in physical implementation from a practical perspective. In this study, we propose a new quantum reservoir computing scheme based on natural physical reservoir on a superconducting quantum device; this system makes full use of the realistic (un-modeled) noise that can couple neighboring qubits. Here, we utilize the IBM superconducting quantum processors to demonstrate the performance for a sequential data processing task; an emulation of the Non-Linear Auto-Regressive Moving Average dynamics (NARMA task). As a result, we observed our proposed model outperformed a simple classical linear regression. This result indicates that a natural physical quantum reservoir realized on a noisy quantum computer is a promising candidate for practical temporal information processing.

DOI: 10.1299/jsmekanto.2021.27.11c01

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