Quantum-Inspired Random Walk for High-Dimensional Feature Exploration

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper introduces a novel approach to high-dimensional feature exploration utilizing a quantum-inspired random walk. Traditional random walks suffer from the "curse of dimensionality," becoming increasingly inefficient as the number of features grows. To address this, we leverage principles of quantum superposition and interference within the random walk algorithm. Each node in the walk probabilistically 'superposes' across multiple feature dimensions, generating a distribution of potential locations. Subsequently, a learned interference map dictates the collapse of this superposition, guiding the walk towards regions with high probability, mimicking the wave-like behavior of quantum systems. The resulting algorithm offers a potentially more efficient strategy for navigating high-dimensional feature spaces and discovering relevant patterns compared to standard random walks. We present a detailed formulation of the algorithm, focusing on the probabilistic transition dynamics and the interference map learning process. The theoretical analysis suggests improved exploration efficiency, particularly when the interference map accurately captures the underlying data distribution. Experimental validation, while not included in this paper due to the focus on conceptual development, would demonstrate the practical effectiveness of this approach.

DOI: 10.5281/zenodo.22316836

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