Noise Analysis of Quantum Approximate Optimization Algorithm on Weighted MAX-CUT

In this paper, we describe the simulation of Ising minimization on a classical machine by executing variational quantum algorithms on our density-matrix simulator. We outline the Ising formulation of the Graph Partitioning problem and the Hamiltonian Cycle problem, and solve the Max-Cut variant of g...

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Bibliographic Details
Published in2019 IEEE Conference on Information and Communication Technology pp. 1 - 6
Main Authors Priyadarshi, Lakshya, Azad, Utkarsh
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2019
Subjects
Adiabatic
Approximation algorithms
Combinatorial Optimization
Ising Model
Non-deterministic Polynomial
Optimization
Program processors
Quantum computing
Quantum entanglement
Variational Quantum Algorithms
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Summary:In this paper, we describe the simulation of Ising minimization on a classical machine by executing variational quantum algorithms on our density-matrix simulator. We outline the Ising formulation of the Graph Partitioning problem and the Hamiltonian Cycle problem, and solve the Max-Cut variant of graph partitioning for a weighted square graph Sq_{2} using the Quantum Approximate Optimization Algorithm. We finally study the effect of errors present in Noisy Intermediate-Scale Quantum processors on the obtained solutions. This paper illustrates the approach to approximately solving hard combinatorial optimization problems using a hybrid quantum-classical scheme and describes the issues in hardware implementation of such schemes. The simulations of NISQ noise models will be useful in understanding the performance and capabilities of such approaches.
DOI:10.1109/CICT48419.2019.9066254