QUBODrivers.jl 🔴🟢🟣🔵

Introduction

This package aims to provide a common MOI-compliant API for QUBO sampling devices. It also contains a few additional tools, including utility samplers and a simple test suite.

QUBO

Problems assigned to solvers defined within QUBODrivers.jl's interface are given by

$$\begin{array}{rl} \text{QUBO}:~ \displaystyle \min_{\mathbf{x}} & \displaystyle \alpha \left[{ \mathbf{x}' Q \mathbf{x} + \ell' \mathbf{x} + \beta }\right] \\ \text{s.t.} & \displaystyle \mathbf{x} \in S \cong \mathbb{B}^{n} \end{array}$$

where $Q \in \mathbb{R}^{n \times n}$ is a strictly upper triangular matrix and $\mathbf{\ell} \in \mathbb{R}^{n}$.

Quick Start

Installation

julia> import Pkg

julia> Pkg.add("QUBODrivers")

Example

using JuMP
using QUBODrivers

model = Model(ExactSampler.Optimizer)

Q = [
    -1.0  2.0  2.0
     2.0 -1.0  2.0
     2.0  2.0 -1.0
]

@variable(model, x[1:3], Bin)
@objective(model, Min, x' * Q * x)

optimize!(model)

for i = 1:result_count(model)
    xi = value.(x; result=i)
    yi = objective_value(model; result=i)
    
    println("f($xi) = $yi")
end

Build your own driver

Sampler authors should start with the Implementing a Sampler tutorial for the @setup and sample interface, packaging checklist, and reference implementations. Use the Test Suite page to wire QUBODrivers.test into your package tests.

List of QUBODrivers.jl-powered packages

Package	Solvers
QUBODrivers.jl	ExactSampler RandomSampler IdentitySampler MIPSampler
QiskitOpt.jl	QiskitOpt.QAOA QiskitOpt.VQE
DWave.jl	DWave DWave.Neal
PySA.jl	PySA
CIMOptimizer.jl	CIMOptimizer
MQLib.jl	MQLib

Badge

If your project is using QUBODrivers.jl, consider adding the official badge to your project's README file:

[![QUBODRIVERS](https://img.shields.io/badge/Powered%20by-QUBODrivers.jl-%20%234063d8)](https://github.com/JuliaQUBO/QUBODrivers.jl)

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