Title: Self-Organizing Quantum Circuits for Signal Processing

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum information processing offers unparalleled potential for advanced signal processing techniques due to its inherent quantum properties. However, the design and implementation of quantum circuits, particularly those requiring dynamic adaptation, remain a significant challenge. This paper introduces a novel approach utilizing Self-Organizing Quantum Circuits (SOQC) – a system of dynamically adjusted quantum circuits – to address this challenge. We propose a reinforcement learning agent to automate the circuit design and parameter optimization process, resulting in highly optimized signal processing capabilities. This work builds upon existing research in quantum control and adaptive circuit design, offering a fundamentally new paradigm for signal processing that leverages the inherent adaptability of quantum systems. The core mechanism involves a reinforcement learning agent learning optimal circuit topologies and parameters through interaction with the input signal. This approach promises to overcome the limitations of traditional, static circuit design methods, leading to enhanced performance and efficiency in signal processing applications. The potential impact of this technology extends across diverse fields, including medical imaging, spectroscopy, and data analysis.

DOI: 10.5281/zenodo.22516557

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