Practical and Scalable Quantum Reservoir Computing

• URL: http://arxiv.org/abs/2405.04799v1
• Date: Wed, 8 May 2024 04:14:31 GMT
• Title: Practical and Scalable Quantum Reservoir Computing
• Authors: Chuanzhou Zhu, Peter J. Ehlers, Hendra I. Nurdin, Daniel Soh,
• Abstract summary: Quantum Reservoir Computing leverages quantum systems to solve complex computational tasks with unprecedented efficiency and reduced energy consumption. This paper presents a novel QRC framework utilizing a quantum optical reservoir composed of two-level atoms within a single-mode optical cavity. We evaluate the reservoir's performance through two primary tasks: the prediction of time-series data via the classification of sine-square waveforms.
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• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum Reservoir Computing leverages quantum systems to solve complex computational tasks with unprecedented efficiency and reduced energy consumption. This paper presents a novel QRC framework utilizing a quantum optical reservoir composed of two-level atoms within a single-mode optical cavity. Employing the Jaynes-Cummings and Tavis-Cummings models, we introduce a scalable and practically measurable reservoir that outperforms traditional classical reservoir computing in both memory retention and nonlinear data processing. We evaluate the reservoir's performance through two primary tasks: the prediction of time-series data via the Mackey-Glass task and the classification of sine-square waveforms. Our results demonstrate significant enhancements in performance with increased numbers of atoms, supported by non-destructive, continuous quantum measurements and polynomial regression techniques. This study confirms the potential of QRC to offer a scalable and efficient solution for advanced computational challenges, marking a significant step forward in the integration of quantum physics with machine learning technology.

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