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We present PyQUBO, an open-source, Python library for constructing quadraticunconstrained binary optimizations (QUBOs) from the objective functions and theconstraints of optimization problems. PyQUBO enables users to prepare QUBOs orIsing models for various combinatorial optimization problems with ease thanksto the abstraction of expressions and the extensibility of the program. QUBOsand Ising models formulated using PyQUBO are solvable by Ising machines,including quantum annealing machines. We introduce the features of PyQUBO withapplications in the number partitioning problem, knapsack problem, graphcoloring problem, and integer factorization using a binary multiplier.Moreover, we demonstrate how PyQUBO can be applied to production-scale problemsthrough integration with quantum annealing machines. Through its flexibilityand ease of use, PyQUBO has the potential to make quantum annealing a morepractical tool among researchers.

PyQUBO: Python Library for Mapping Combinatorial Optimization Problems to QUBO Form