Symbolic Regression with Quantum Annealing for Discovering New Mathematical Equations

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

This paper explores the application of quantum annealing to the field of symbolic regression, a technique for automatically discovering mathematical equations from data. The core idea is to frame the symbolic regression problem as a quadratic unconstrained binary optimization (QUBO) problem, which can then be solved using the quantum annealing algorithm. This approach aims to identify previously unknown mathematical relationships that accurately represent a given dataset. The paper details the methodology, including the mapping process, the QUBO formulation, and the utilization of the quantum annealing solver. We demonstrate, through theoretical analysis and conceptual design, the potential for quantum annealing to outperform classical methods in certain scenarios, particularly when dealing with complex datasets and high-dimensional search spaces. The resulting equations are presented as a set of symbolic expressions, enabling both quantitative analysis and further investigation. The ultimate goal is to provide a framework for automated equation discovery, potentially leading to advancements in various scientific and engineering domains.

DOI: 10.5281/zenodo.22241715

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