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The NISQ Analyzer: Automating the Selection of Quantum Computers for Quantum Algorithms

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Service-Oriented Computing (SummerSOC 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1310))

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Abstract

Quantum computing can enable a variety of breakthroughs in research and industry in the future. Although some quantum algorithms already exist that show a theoretical speedup compared to the best known classical algorithms, the implementation and execution of these algorithms come with several challenges. The input data determines, for example, the required number of qubits and gates of a quantum algorithm. A quantum algorithm implementation also depends on the used Software Development Kit which restricts the set of usable quantum computers. Because of the limited capabilities of current quantum computers, choosing an appropriate one to execute a certain implementation for a given input is a difficult challenge that requires immense mathematical knowledge about the implemented quantum algorithm as well as technical knowledge about the used Software Development Kits. In this paper, we present a concept for the automated analysis and selection of implementations of quantum algorithms and appropriate quantum computers that can execute a selected implementation with a certain input data. The practical feasibility of the concept is demonstrated by the prototypical implementation of a tool that we call NISQ Analyzer.

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Notes

  1. 1.

    https://www.ibm.com.

  2. 2.

    https://www.rigetti.com.

  3. 3.

    https://www.ibm.com/blogs/research/2020/09/ibm-quantum-roadmap/.

  4. 4.

    https://ionq.com/news/october-01-2020-most-powerful-quantum-computer.

  5. 5.

    https://qiskit.org.

  6. 6.

    http://docs.rigetti.com/en/stable/.

  7. 7.

    https://quantum-computing.ibm.com.

  8. 8.

    https://github.com/UST-QuAntiL/nisq-analyzer.

  9. 9.

    https://www.swi-prolog.org.

  10. 10.

    https://github.com/UST-QuAntiL/qiskit-service.

  11. 11.

    https://github.com/Qiskit.

  12. 12.

    https://qiskit.org/documentation/stubs/qiskit.compiler.transpile.html.

  13. 13.

    https://python-rq.org.

  14. 14.

    https://github.com/UST-QuAntiL/nisq-analyzer-content.

  15. 15.

    https://qiskit.org/documentation/apidoc/qiskit.aqua.algorithms.html.

  16. 16.

    https://quantum-computing.ibm.com/docs/manage/account/ibmq.

  17. 17.

    https://qiskit.org/documentation/stubs/qiskit.aqua.components.oracles.TruthTableOracle.html.

  18. 18.

    https://quantum-circuit.com/app_details/HYLMtcuK6b7uaphC7.

  19. 19.

    https://planqk.de/en/.

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Acknowledgements

This work was partially funded by the BMWi project PlanQK (01MK20005N) and the DFG’s Excellence Initiative project SimTech (EXC 2075 - 390740016).

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Authors and Affiliations

  1. Institute of Architecture of Application Systems, University of Stuttgart, Universitätsstraße 38, Stuttgart, Germany

    Marie Salm, Johanna Barzen, Uwe Breitenbücher, Frank Leymann, Benjamin Weder & Karoline Wild

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  1. Marie Salm
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  2. Johanna Barzen
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  4. Frank Leymann
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Correspondence to Marie Salm .

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  1. Distributed Systems Group, Technische Universität Wien, Vienna, Austria

    Schahram Dustdar

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Salm, M., Barzen, J., Breitenbücher, U., Leymann, F., Weder, B., Wild, K. (2020). The NISQ Analyzer: Automating the Selection of Quantum Computers for Quantum Algorithms. In: Dustdar, S. (eds) Service-Oriented Computing. SummerSOC 2020. Communications in Computer and Information Science, vol 1310. Springer, Cham. https://doi.org/10.1007/978-3-030-64846-6_5

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  • DOI: https://doi.org/10.1007/978-3-030-64846-6_5

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