Title: Dimensionality Reduction via Wavefunction Sampling with Constraints

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Dimensionality reduction is a fundamental challenge in many data science applications, requiring efficient methods to represent high-dimensional data while preserving essential information. Traditional dimensionality reduction techniques often suffer from limitations in preserving data integrity and can result in loss of interpretability. This paper proposes a novel approach to dimensionality reduction using wavefunction sampling, coupled with a sophisticated constraint satisfaction mechanism. We explore the potential of this method to uncover hidden structure within the data space, offering a pathway towards more meaningful and robust representations. The core of our system is a quantum state sampling algorithm that dynamically adjusts the probability distribution of wavefunction states based on predefined constraints, ensuring that the resulting representations accurately capture the underlying data patterns. We present a comprehensive analysis of the algorithm's performance, demonstrating its effectiveness in reducing data dimensionality while maintaining a high degree of data fidelity. This work holds promise for applications in various fields, including image processing, natural language processing, and data visualization.

DOI: 10.5281/zenodo.22492423

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