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Quantum-Enhanced Constraint Propagation for Sparse Data

Jincheng Zhang

Published Sep 6, 2026• 0 citations
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Abstract

This paper investigates the potential of quantum annealing and Grover's algorithm to enhance constraint propagation for sparse data sets. Sparse data presents significant computational challenges in many applications, particularly in machine learning and optimization problems. Traditional constraint propagation methods often struggle with this data, leading to increased computational costs. We propose an algorithm leveraging quantum annealing to efficiently explore the sparse solution space, reducing the computational burden. The core mechanism involves employing quantum algorithms to accelerate the search for optimal solutions, ultimately improving performance. This work explores the theoretical benefits and potential practical applications of this approach.

DOI
10.5281/zenodo.22508679

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