Multi-Scale Quantum Information Representation Learning

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

This paper investigates the potential of multi-scale approaches to quantum information representation learning. Traditional quantum information representation methods often operate at a single scale, neglecting the inherent multi-scale nature of quantum data. We propose a framework for learning quantum information representations by decomposing the information into various scales and subsequently learning a distinct feature representation for each scale. These representations are then fused to create a comprehensive multi-scale quantum representation. We hypothesize that this multi-scale approach will enable more effective extraction and representation of key quantum information features, ultimately improving the efficiency of quantum computation. The core claim of this work is that utilizing multi-scale analysis methods can significantly enhance the effectiveness of extracting and representing crucial quantum information characteristics, leading to improved quantum computing performance. The central mechanism involves the decomposition of quantum information into distinct scales, followed by the learning of feature representations for each scale, culminating in the fusion of these representations to establish a robust multi-scale quantum information representation. This research addresses a critical limitation in current quantum information processing by acknowledging and leveraging the multi-scale properties of quantum data.

DOI: 10.5281/zenodo.22153381

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