Utilizing natural physical reservoir realized on noisy superconducting quantum processors for temporal information processing
Yudai Suzuki, Qi Gao, Kenji Yasuoka, Naoki Yamamoto · The Proceedings of Conference of Kanto Branch · 2021
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.