Title: Adaptive Representation of Quantum States using Quantum Reservoir Networks

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum state representations are fundamental to quantum information processing, yet current methods often struggle with inherent complexities and require substantial computational resources. This paper introduces an adaptive representation of quantum states leveraging Quantum Reservoir Networks (QRNs). QRNs are dynamically constructed networks of interconnected nodes that evolve their connections to learn and represent quantum states. This approach offers a novel framework for efficient computation and manipulation of quantum information, mitigating the limitations of traditional representations. The core mechanism involves a feedback loop that adjusts the network's structure based on the quantum system's state, dynamically optimizing the representation for specific tasks. We demonstrate the efficacy of this approach through a series of simulations and analyses, highlighting its potential for accelerating quantum algorithm development and enabling more efficient quantum information handling.

DOI: 10.5281/zenodo.22513697

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