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PyQUBO: Python Library for Mapping Combinatorial Optimization Problems to QUBO Form
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DOI：10.1109/TC.2021.3063618.png)
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Abstract
En 中文
We present PyQUBO, an open-source Python library for constructing quadratic unconstrained binary optimizations (QUBOs) from the objective functions and the constraints of optimization problems. PyQUBO enables users to prepare QUBOs or Ising models for various combinatorial optimization problems with ease thanks to the abstraction of expressions and the extensibility of the program. QUBOs and Ising models formulated using PyQUBO are solvable by Ising machines, including quantum annealing machines. We introduce the features of PyQUBO with applications in the number partitioning problem, knapsack problem, graph coloring problem, and integer factorization using a binary multiplier. Moreover, we demonstrate how PyQUBO can be applied to production-scale problems through integration with quantum annealing machines. Through its flexibility and ease of use, PyQUBO has the potential to make quantum annealing a more practical tool among researchers.
Keywords:
Optimization
Annealing
Mathematical model
Linear programming
Python
Cost function
Computational modeling
Quantum annealing
QUBO
Ising machine
combinatorial optimization
Python
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