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  4. A Comparative Study on Solving Optimization Problems with Exponentially Fewer Qubits
 
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2024
Journal Article
Title

A Comparative Study on Solving Optimization Problems with Exponentially Fewer Qubits

Abstract
Variational quantum optimization algorithms, such as the variational quantum eigensolver (VQE) or the quantum approximate optimization algorithm (QAOA), are among the most studied quantum algorithms. In our work, we evaluate and improve an algorithm based on the VQE, which uses exponentially fewer qubits compared to the QAOA. We highlight the numerical instabilities generated by encoding the problem into the variational ansatz and propose a classical optimization procedure to find the ground state of the ansatz in fewer iterations with a better or similar objective. In addition, we propose a method to embed the linear interpolation of the MaxCut problem on a quantum device. Furthermore, we compare classical optimizers for this variational ansatz on quadratic unconstrained binary optimization and graph partitioning problems.
Author(s)
Winderl, David
Fraunhofer-Institut für Kognitive Systeme IKS  
Franco, Nicola  
Fraunhofer-Institut für Kognitive Systeme IKS  
Lorenz, Jeanette Miriam  orcid-logo
Fraunhofer-Institut für Kognitive Systeme IKS  
Journal
IEEE transactions on quantum engineering  
Project(s)
BayQC-Hub
Funder
Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie  
Open Access
DOI
10.1109/TQE.2024.3392834
Language
English
Fraunhofer-Institut für Kognitive Systeme IKS  
Fraunhofer Group
Fraunhofer-Verbund IUK-Technologie  
Keyword(s)
  • optimization

  • hybrid algorithm

  • quantum computing

  • Variational Quantum Eigensolver

  • VQE

  • quantum approximate optimization algorithm

  • QAOA

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