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

Mashiyat Zaman, Kotaro Tanahashi, Shu Tanaka

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)838-850
Number of pages13
JournalIEEE Transactions on Computers
Issue number4
Publication statusPublished - 2022 Apr 1


  • Ising machine
  • Python
  • QUBO
  • Quantum annealing
  • combinatorial optimization

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture
  • Computational Theory and Mathematics


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