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arXiv:2001.09498 · Physical Review Applied

Temporal Information Processing on Noisy Quantum Computers

Jiayin Chen, Hendra I. Nurdin, Naoki Yamamoto

Published Aug 24, 2020• 120 citations
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Abstract

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.

DOI
10.1103/physrevapplied.14.024065

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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.
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.
Universal fault-tolerant quantum computers require millions of qubits with low error rates. Since this technology is years ahead, noisy intermediate-scale quantum (NISQ) computation is receiving tremendous interest. In this setup, quantum reservoir computing is a relevant machine learning algorithm. Its simplicity of training and implementation allows to perform challenging computations on today's available machines. In this Letter, we provide a criterion to select optimal quantum reservoirs, requiring few and simple gates. Our findings demonstrate that they render better results than other commonly used models with significantly less gates and also provide insight on the theoretical gap between quantum reservoir computing and the theory of quantum states' complexity.
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.
Quantum learning provides a versatile paradigm for information processing by exploiting the intrinsic representational capacity of high-dimensional Hilbert spaces. Here, we investigate a Hamiltonian-encoding framework for quantum reservoir computing that simultaneously addresses three key challenges in quantum learning: trainability, hardware efficiency, and information stability. In this framework, input data are directly mapped onto a fixed Hamiltonian and transformed into expressive nonlinear features through quantum dynamical evolution. By employing the reservoir-computing paradigm, the approach naturally circumvents the barren plateau problem in quantum learning landscapes. We validate the framework across two complementary platforms: an analog superconducting array processor and a digital gate-based quantum circuit implementation. Despite their fundamentally different realizations, both platforms exhibit comparable representational power and achieve competitive learning performance, establishing a unified framework for cross-platform quantum learning. While both implementations achieve comparable performance, the analog processor may offer a more hardware-efficient realization by bypassing the temporal overhead of gate-based decomposition and thereby making more effective use of finite coherence times, albeit at the expense of universality. Furthermore, we find that finite dissipation suppresses quantum-scrambling-induced instabilities at long evolution times and can enhance learning performance, revealing a constructive role for environmental coupling in stabilizing quantum learning dynamics. Collectively, these results establish Hamiltonian-encoded reservoir computing as a compact, expressive, and hardware-efficient paradigm for quantum learning on current-generation quantum platforms.
Universal fault-tolerant quantum computers require millions of qubits with low error rates. Since this technology is years ahead, noisy intermediate-scale quantum (NISQ) computation is receiving tremendous interest. In this setup, quantum reservoir computing is a relevant machine learning algorithm. Its simplicity of training and implementation allows to perform challenging computations on today available machines. In this Letter, we provide a criterion to select optimal quantum reservoirs, requiring few and simple gates. Our findings demonstrate that they render better results than other commonly used models with significantly less gates, and also provide insight on the theoretical gap between quantum reservoir computing and the theory of quantum states complexity.