Quantum Reservoir Computing with Analog Noise

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper proposes a novel approach to prediction tasks utilizing Quantum Reservoir Computing (QRC) leveraging carefully controlled analog noise within a quantum system. Traditional QRC relies heavily on complex, algorithm-driven manipulation of the reservoir, often demanding significant computational overhead. This work posits that analog noise, when strategically applied and processed, can serve as an efficient and robust computational resource, surpassing the performance of conventional digital methods. The core concept involves a specifically designed quantum reservoir – typically a network of coupled qubits – subjected to precisely engineered analog noise. This interaction facilitates non-linear feature extraction directly within the quantum system, eliminating the need for intricate algorithm design. The paper outlines the theoretical framework, exploring the relationship between noise characteristics, reservoir dynamics, and prediction accuracy. We demonstrate, through mathematical formulation, that specific noise profiles can dramatically improve prediction outcomes. The ultimate goal is to demonstrate a pathway toward more accessible and efficient quantum-based prediction, potentially reducing the barriers to adoption within various applications.

DOI: 10.5281/zenodo.22636634

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