Ribonucleic acid (RNA) molecules play informational, structural, and metabolic roles in all living cells. RNAs are chains of nucleotides containing bases {A, C, G, U} that interact via base pairings to determine higher order structure and functionality. The RNA folding problem is to predict one or more secondary RNA structures from a given primary sequence of bases. From a mathematical modeling perspective, solutions to the RNA folding problem come from minimizing the thermodynamic free energy of a structure by selecting which bases will be paired, subject to a set of constraints. Here we report on a Quadratic Unconstrained Binary Optimization (QUBO) modeling paradigm that fits naturally with the parameters and constraints required for RNA folding prediction. Three QUBO models are presented along with a hybrid metaheuristic algorithm. Extensive testing results show a strong positive correlation with benchmark results.

核糖核酸（RNA）分子在所有活细胞中均起信息，结构和代谢作用。RNA是包含碱基{A，C，G，U}的核苷酸链，它们通过碱基配对相互作用以确定更高阶的结构和功能。RNA折叠问题是根据给定的一级碱基序列预测一个或多个二级RNA结构。从数学建模的角度来看，RNA折叠问题的解决方案是通过选择要配对的碱基而受到一组约束，从而使结构的热力学自由能最小化。在这里，我们报告一个二次无约束二进制优化（QUBO）建模范例，该范例自然适合RNA折叠预测所需的参数和约束。提出了三种QUBO模型以及一种混合元启发式算法。