Hyperdimensional Computing for Quantum State Tomography

Jincheng Zhang

Published Aug 28, 2026• 0 citations

Abstract

Quantum state tomography (QST) is a fundamental technique for characterizing quantum systems, but it often suffers from significant experimental challenges, including high measurement complexity and resource demands. This paper explores the potential of hyperdimensional computing (HDC) as a viable alternative to traditional QST. HDC leverages high-dimensional vectors to represent and manipulate quantum states, allowing for the development of algorithms that exploit inherent correlations to reconstruct quantum state information with reduced measurement requirements. The core claim is that HDC offers a potentially efficient and scalable approach to QST. This work details the theoretical framework, outlining key algorithms and highlighting the advantages of utilizing HDC for quantum state characterization. The focus is on demonstrating the feasibility of this approach through conceptual design, emphasizing the reduced complexity compared to conventional QST methods. We propose a framework for representing quantum states within HDC vectors and present a simplified reconstruction algorithm.

DOI: 10.5281/zenodo.22139800

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