QUBO.jl

A Julia ecosystem for Quadratic Unconstrained Binary Optimization

QUBO.jl is an all-in-one package for working with QUBO models in JuMP and interfacing with their solvers. This project aggregates and extends functionality from its complementary packages ToQUBO.jl, QUBODrivers.jl and QUBOTools.jl.

QUBO is an acronym for Quadratic Unconstrained Binary Optimization, a notorious family of Combinatorial Optimization problems. Recently, significant advances in computing systems and algorithms specialized for sampling QUBO solutions have contributed to its increasing popularity.

These novel tools include Quantum Annealing, Quantum Gate-Circuit Optimization Algorithms (Quantum Optimization Alternating Ansatz, Variational Quantum Eigensolver), other hardware-accelerated platforms, such as Coherent Ising Machines and Simulated Bifurcation Machines, not to mention traditional methods such as Simulated Annealing and Parallel Tempering.

Conceptually speaking, it is an optimization model with a quadratic objective function on binary variables and no constraints. Despite being very simple, these models are capable of representing other nonconvex global optimization problems.

import Pkg

Pkg.add("QUBO")

Given the following binary Knapsack Problem

one could write a simple JuMP model and have its constraint automatically encoded by ToQUBO.jl.

using JuMP
using QUBO

model = Model(() -> ToQUBO.Optimizer(ExactSampler.Optimizer))

@variable(model, x[1:3], Bin)
@objective(model, Max, x[1] + 2 * x[2] + 3 * x[3])
@constraint(model, 0.3 * x[1] + 0.5 * x[2] + x[3] <= 1.6)

optimize!(model)

for i = 1:result_count(model)
    xi = value.(x, result = i)
    yi = objective_value(model, result = i)

    println("x: ", xi, "; cost = ", yi)
end

ToQUBO.jl

ToQUBO.jl is a Julia package to reformulate general optimization problems into QUBO (Quadratic Unconstrained Binary Optimization) instances. This tool aims to convert a broad range of JuMP problems for straightforward application in many physics and physics-inspired solution methods whose normal optimization form is equivalent to the QUBO. Not only it is has the widest constraint coverage but also is the most performant QUBO reformulation tool available.

During execution, ToQUBO.jl encodes both discrete and continuous variables, maps constraints, and computes their penalties, performing a few model optimization steps along the process. ToQUBO.jl was written as a MathOptInterface (MOI) layer that automatically maps between input and output models, thus providing a smooth JuMP modeling experience.

QUBODrivers.jl

This package aims to provide a common MOI-compliant API for QUBO Sampling and Annealing machines. It also contains testing tools, including utility samplers for performance comparison and sanity checks.

It was designed to allow algorithm developers and hardware manufacturers to easily connect their products to the JuMP ecosystem. Its simple interface paves the path for the rapid integration of heterogeneous QUBO solvers, including cloud-based Quantum Computing services (DWave.jl, QiskitOpt.jl); Quantum Simulation software (QuantumAnnealingInterface.jl; CIMOptimizer.jl) and Heuristic solvers (MQLib.jl, DWaveNeal.jl).

QUBOTools.jl

The QUBOTools.jl package implements a broad set of utilities for working with QUBO instances. It defines the abstract interfaces for representing both QUBO models and their solutions. Besides that, its library contains reference implementations for the proposed interface, making it ready to power other applications.

One of its main purposes is to provide fast and reliable conversion mechanism between common file formats for storing such problems. With QUBOTools.jl it is possible to read models from various benchmarking databases and also write models in specifications that most devices will directly handle.

Citing QUBO.jl 📑

If you find QUBO.jl and its packages useful in your work, we kindly request that you cite the following paper (preprint):

@misc{qubojl:2023,
  title         = {QUBO.jl: A Julia Ecosystem for Quadratic Unconstrained Binary Optimization}, 
  author        = {Pedro {Maciel Xavier} and Pedro Ripper and Tiago Andrade and Joaquim {Dias Garcia} and Nelson Maculan and David E. {Bernal Neira}},
  year          = {2023},
  doi           = {10.48550/arXiv.2307.02577},
  eprint        = {2307.02577},
  archivePrefix = {arXiv},
  primaryClass  = {math.OC},
}

This project is part of a collaboration involving PSR Energy Consulting & Analytics, the Federal University of Rio de Janeiro (UFRJ), Purdue University and the Universities Space Research Association (USRA).