Preprint Article Version 2 This version is not peer-reviewed

Applying Quantum Optimization Algorithms for Linear Programming

Version 1 : Received: 30 March 2017 / Approved: 31 March 2017 / Online: 31 March 2017 (12:29:33 CEST)
Version 2 : Received: 13 April 2017 / Approved: 14 April 2017 / Online: 14 April 2017 (04:16:51 CEST)
Version 3 : Received: 12 May 2017 / Approved: 16 May 2017 / Online: 16 May 2017 (12:31:40 CEST)

How to cite: Side, M.; Erol, V. Applying Quantum Optimization Algorithms for Linear Programming. Preprints 2017, 2017030238 (doi: 10.20944/preprints201703.0238.v2). Side, M.; Erol, V. Applying Quantum Optimization Algorithms for Linear Programming. Preprints 2017, 2017030238 (doi: 10.20944/preprints201703.0238.v2).

Abstract

Quantum computers are machines that are designed to use quantum mechanics in order to improve upon classical computers by running quantum algorithms. One of the main applications of quantum computing is solving optimization problems. For addressing optimization problems we can use linear programming. Linear programming is a method to obtain the best possible outcome in a special case of mathematical programming. Application areas of this problem consist of resource allocation, production scheduling, parameter estimation, etc. In our study, we looked at the duality of resource allocation problems. First, we chose a real world optimization problem and looked at its solution with linear programming. Then, we restudied this problem with a quantum algorithm in order to understand whether if there is a speedup of the solution. The improvement in computation is analysed and some interesting results are reported.

Subject Areas

linear programming; optimization; quantum algorithms; complexity

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