Quantum-Inspired Symbolic Regression with Adaptive Entanglement

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Symbolic regression aims to discover mathematical equations from data, a task crucial in scientific discovery and model building. However, traditional symbolic regression methods often falter when dealing with high-dimensional, noisy datasets, plagued by the "curse of dimensionality" and the difficulty in identifying underlying patterns. This paper introduces a novel approach, Quantum-Inspired Symbolic Regression with Adaptive Entanglement (QISREA), designed to overcome these limitations. QISREA draws inspiration from quantum computing principles, particularly adaptive entanglement, to efficiently explore the vast search space of possible symbolic expressions. The core idea involves dynamically adjusting entanglement levels among potential terms in the equation based on the estimated data uncertainty. Higher entanglement signifies greater computational focus on regions of the search space deemed more promising, effectively mitigating the curse of dimensionality. We present a detailed formulation of the algorithm, outlining the key components including the initial entanglement distribution, the adaptive entanglement update rule, and the equation evaluation process. The effectiveness of QISREA is demonstrated through theoretical analysis and a conceptual discussion, highlighting its potential advantages over conventional symbolic regression techniques.

DOI: 10.5281/zenodo.22195200

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