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In this repository will be realized comparison between methods of linear optimization with algorithms classical versus approach quantum annealing using the machine of D-Wave System.

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valteresj2/Quantum-Solving-Linear-Optimization

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Install

The Python used in this project was 3.6.5, you can install the necessary packages by 'pip install -r requirements.txt'.

Optimization Linear Apply in Quantum Computer

In this project have finally has the purpose to bring a direct conversion of the processes of linear optimization to a quadratic form with application in quantum computers of D-Wave.

In the repository there are three .py files, the file optimize_quantum.py is a class of functions they are:

[1] product_notable
[2] model_dwave
[3] result_dwave

The function [1] transforms linear constraints into quadratic functions and sums all constraints, function [2] transforms the final quadratic function into the D-Wave model and finally the function [3] brings the final solution with the lowest energy and the best value that maximizes or minimizes according to the objective function.

The example_article_linkedin.py file runs an example based on the mathematical model below:

\Large \begin{aligned} & \text{maximize} & & 7x_1+4x_2+19x_3 \ & \text{subject to} && x_1+x_3\leq1, \ & &&   x_2+x_3 \leq1, \ & &&  x_1,x_2,x_3=0 \enspace or \enspace 1. \end{aligned}

For more details read the introduction in the file (Introduction D-Wave Linear Optimization) in pdf format. Suggestions or constructive criticism send an email to valteresj2@gmail.com or rmd2.cin@gmail.com.

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In this repository will be realized comparison between methods of linear optimization with algorithms classical versus approach quantum annealing using the machine of D-Wave System.

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