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arXiv:2107.05808 · Scientific Reports

Natural quantum reservoir computing for temporal information processing

Yudai Suzuki, Qi Gao, Ken C. Pradel, Kenji Yasuoka, Naoki Yamamoto

Published Jan 25, 2022• 81 citations
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Abstract

Reservoir computing is a temporal information processing system that exploits artificial or physical dissipative dynamics to learn a dynamical system and generate the target time-series. This paper proposes the use of real superconducting quantum computing devices as the reservoir, where the dissipative property is served by the natural noise added to the quantum bits. The performance of this natural quantum reservoir is demonstrated in a benchmark time-series regression problem and a practical problem classifying different objects based on temporal sensor data. In both cases the proposed reservoir computer shows a higher performance than a linear regression or classification model. The results indicate that a noisy quantum device potentially functions as a reservoir computer, and notably, the quantum noise, which is undesirable in the conventional quantum computation, can be used as a rich computation resource.

DOI
10.1038/s41598-022-05061-w

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Reservoir computing is a temporal information processing system that exploits artificial or physical dissipative dynamics to learn a dynamical system and generate the target time-series. This paper proposes the use of real superconducting quantum computing devices as the reservoir, where the dissipative property is served by the natural noise added to the quantum bits. The performance of this natural quantum reservoir is demonstrated in a benchmark time-series regression problem and a practical problem classifying different objects based on temporal sensor data. In both cases the proposed reservoir computer shows a higher performance than a linear regression or classification model. The results indicate that a noisy quantum device potentially functions as a reservoir computer, and notably, the quantum noise, which is undesirable in the conventional quantum computation, can be used as a rich computation resource.
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.
Reservoir computing is a machine learning framework that uses artificial or physical dissipative dynamics to predict time-series data using nonlinearity and memory properties of dynamical systems. Quantum systems are considered promising physical reservoirs, and several quantum reservoir computing (QRC) schemes that exploit intrinsic quantum mechanical properties as computational resources have been proposed. This paper focuses on a particular QRC scheme that was theoretically proposed to use the repeated quantum non-demolition measurement to generate a time series, mainly for the purpose of reducing the total execution time compared to the conventional method without repeated measurement. We experimentally implemented this scheme on IBM superconducting devices and demonstrated that shorter execution time and higher accuracy can be achieved than with a specific conventional method. Then, we study the temporal information processing capacity to quantify the computational capability of the proposed QRC; in particular, we use this quantity to identify the measurement strength that finds the best balance between the amount of available information and the strength of dissipation. An experimental demonstration with a soft robot is also provided, where repeated measurement over 1,000 timesteps was effectively implemented. Finally, we implemented the repeated-measurement QRC scheme using 120 qubits, to test the potential computational power of a large-scale QRC.
We analyze numerically the performance of Quantum Reservoir Computing (QRC) for statistical and financial problems. We use a reservoir composed of two superconducting islands coupled via their charge degrees of freedom. The key non-linear elements that provide the reservoir with rich and complex dynamics are the Josephson junctions that connect each island to the ground. We show that QRC implemented in this circuit can accurately classify complex probability distributions, including those with heavy tails, and identify regimes in correlated time series, such as periods of high volatility generated by standard econometric models. We find QRC to outperform some of the best classical methods when the amount of information is limited. This demonstrates its potential to be a noise-resilient quantum learning approach capable of tackling real-world problems within currently available superconducting platforms. We further discuss how to improve our QRC algorithm in real superconducting hardware to benefit from a much larger Hilbert space.
Reservoir computing is a machine learning paradigm that exploits nonlinear dissipative dynamical systems for temporal information processing, and can be combined with quantum computing to form quantum reservoir computers. This study proposes a class of quantum reservoir computers that can be implemented on noisy intermediate-scale quantum (NISQ) computers and possesses the properties required to be reservoir computers, especially universality. Efficient implementation and proof-of-principle demonstration on several cloud-based IBM superconducting quantum devices suggest that the proposed scheme could lead to promising applications of NISQ computers.
Physical reservoir computing provides a powerful machine learning paradigm that exploits nonlinear physical dynamics for efficient information processing. By incorporating quantum effects, quantum reservoir computing offers superior potential for machine learning applications, as quantum dynamics are exponentially costly to simulate classically. Here, we present a novel quantum reservoir computing approach based on correlated quantum spin systems, exploiting natural quantum many-body interactions to generate reservoir dynamics, thereby circumventing the practical challenges of deep quantum circuits. Our experimental implementation supports nontrivial quantum entanglement and exhibits sufficient dynamical complexity for high-performance machine learning. We achieve state-of-the-art performance in experiments on standard time-series benchmarks, reducing prediction error by 1 to 2 orders of magnitude compared to previous quantum reservoir experiments. In long-term weather forecasting, our 9-spin quantum reservoir delivers greater prediction accuracy than classical reservoirs with thousands of nodes. This represents the first experimental demonstration of quantum machine learning outperforming large-scale classical models on real-world tasks.
This paper investigates the application of quantum reservoir computing (QRC) to time series prediction. Classical reservoir computing (RC) utilizes a complex, high-dimensional system – the 'reservoir' – as a non-linear feature extractor, followed by a linear regression model for prediction. QRC extends this concept by leveraging the inherent dynamics of a quantum system as the reservoir. We hypothesize that quantum systems, due to their superposition and entanglement properties, can provide richer and more complex dynamics than classical reservoirs, potentially leading to improved prediction accuracy. This work outlines the theoretical framework for QRC, detailing the setup of a suitable quantum reservoir (e.g., a superconducting qubit array), the measurement process, and the training strategy involving a classical machine learning model. We demonstrate a conceptual approach, focusing on the potential advantages and challenges of employing quantum systems for this task. The core claim is that QRC can achieve superior time series prediction accuracy compared to classical RC methods. Key elements explored include the input signal's influence on the quantum reservoir, the selection of appropriate measurement schemes, and the choice of classical learning algorithms. The paper concludes with a discussion of future research directions and the potential impact of QRC on various time series prediction applications.