论文标题
如何通过二次不受约束的二进制优化近似任何目标函数
How to Approximate any Objective Function via Quadratic Unconstrained Binary Optimization
论文作者
论文摘要
二次无约束的二进制优化（QUBO）已成为使用量子计算机（即用于量子近似优化算法（QAOA）和量子退火（QA）的量子计算机的标准格式。我们提出了一种方法，可以通过（i）将它们近似为多项式，然后（ii）将任何多项式转换为QUBO，将几乎任意问题转换为QUBO。我们在两个示例问题（削减比率和逻辑回归）上展示了方法的用法。
Quadratic unconstrained binary optimization (QUBO) has become the standard format for optimization using quantum computers, i.e., for both the quantum approximate optimization algorithm (QAOA) and quantum annealing (QA). We present a toolkit of methods to transform almost arbitrary problems to QUBO by (i) approximating them as a polynomial and then (ii) translating any polynomial to QUBO. We showcase the usage of our approaches on two example problems (ratio cut and logistic regression).
