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  4. Recommending Solution Paths for Solving Optimization Problems with Quantum Computing
 
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2023
Conference Paper
Title

Recommending Solution Paths for Solving Optimization Problems with Quantum Computing

Abstract
Solving real-world optimization problems with quantum computing requires choosing between a large number of options concerning formulation, encoding, algorithm and hardware. Finding good solution paths is challenging for end users and researchers alike. We propose a framework designed to identify and recommend the best-suited solution paths in an automated way. This introduces a novel abstraction layer that is required to make quantum-computing-assisted solution techniques accessible to end users without requiring a deeper knowledge of quantum technologies. State-of-the-art hybrid algorithms, encoding and decomposition techniques can be integrated in a modular manner and evaluated using problem-specific performance metrics. Equally, tools for the graphical analysis of variational quantum algorithms are developed. Classical, fault tolerant quantum and quantum-inspired methods can be included as well to ensure a fair comparison resulting in useful solution paths. We demonstrate and validate our approach on a selected set of options and illustrate its application on the capacitated vehicle routing problem (CVRP). We also identify crucial requirements and the major design challenges for the proposed abstraction layer within a quantum-assisted solution workflow for optimization problems.
Author(s)
Poggel, Benedikt  orcid-logo
Fraunhofer-Institut für Kognitive Systeme IKS  
Quetschlich, Nils
Technische Universität München  
Burgholzer, Lukas
Johannes Kepler Universität Linz  
Wille, Robert
Technische Universität München  
Lorenz, Jeanette Miriam  orcid-logo
Fraunhofer-Institut für Kognitive Systeme IKS  
Mainwork
IEEE International Conference on Quantum Software, QSW 2023. Proceedings  
Project(s)
QuaST
Funder
Bundesministerium für Wirtschaft und Klimaschutz  
Conference
International Conference on Quantum Software 2023  
Open Access
DOI
10.1109/QSW59989.2023.00017
Language
English
Fraunhofer-Institut für Kognitive Systeme IKS  
Fraunhofer Group
Fraunhofer-Verbund IUK-Technologie  
Keyword(s)
  • quantum computing

  • applied optimization

  • hybrid quantum-classical algorithm

  • variational quantum algorithm

  • design automation

  • abstraction layer

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