Quantum-Inspired Symbolic Computation with Tensor Networks

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper explores a novel approach to symbolic computation inspired by quantum computing principles, utilizing tensor networks to represent and manipulate symbolic expressions. The core idea is to map complex symbolic calculations onto the structure of tensor networks, capitalizing on the inherent parallelism and entanglement within these networks to dramatically reduce computational complexity. The proposed method enables efficient parallel processing of operations on tensor components, minimizing redundant calculations through entanglement exploitation. This approach presents a potentially significant speedup for intricate symbolic computations, bridging the gap between traditional symbolic methods and the computational advantages offered by quantum algorithms. The paper outlines the key concepts, the mapping strategy, and the theoretical underpinnings of this quantum-inspired symbolic computation paradigm.

DOI: 10.5281/zenodo.22334496

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