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Optimizing the Prioritization of Compiled Quantum Circuits by Machine Learning Approaches

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Service-Oriented Computing (SummerSOC 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1603))

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Abstract

The performance of current quantum computers is limited by high error rates and few qubits. Nevertheless, more and more quantum computers are available in the cloud. Selecting a suitable quantum computer to execute a specific quantum circuit and receive precise results can be difficult. At the same time, it is crucial to choose an available quantum computer that offers the hardware characteristics required by the circuit to retrieve precise results, depending on the quantum computer’s last re-calibration and the quantum compiler that maps the circuit to the hardware. Furthermore, cloud providers regulate hardware access, so waiting times must be considered. To support the choice of a quantum computer, we introduced an automated framework in previous work. It enables the user to analyze and prioritize the compiled circuits of a given input circuit for different quantum computers based on their requirements. In this work, we extend the framework by automating the prioritization of compiled circuits targeting short waiting times and precise executions based on previous results. We present our framework’s prototype and case study to demonstrate and evaluate the practical feasibility.

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Notes

  1. 1.

    https://youtu.be/luSWN5SRxNg.

  2. 2.

    https://pypi.org/project/pymcdm/.

  3. 3.

    https://docs.scipy.org/doc/scipy/reference/optimize.minimize-cobyla.html.

  4. 4.

    https://github.com/UST-QuAntiL/nisq-analyzer-content/tree/paper/optimizing-prioritization/benchmarking.

  5. 5.

    https://qiskit.org/textbook/ch-quantum-hardware/randomized-benchmarking.html.

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Acknowledgements

This work was partially funded by the BMWK project PlanQK (01MK20005N).

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

    Marie Salm, Johanna Barzen, Frank Leymann & Philipp Wundrack

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  1. Marie Salm
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Correspondence to Marie Salm .

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  1. University of Stuttgart, Stuttgart, Germany

    Johanna Barzen

  2. University of Stuttgart, Stuttgart, Germany

    Frank Leymann

  3. TU Wien, Vienna, Austria

    Schahram Dustdar

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Salm, M., Barzen, J., Leymann, F., Wundrack, P. (2022). Optimizing the Prioritization of Compiled Quantum Circuits by Machine Learning Approaches. In: Barzen, J., Leymann, F., Dustdar, S. (eds) Service-Oriented Computing. SummerSOC 2022. Communications in Computer and Information Science, vol 1603. Springer, Cham. https://doi.org/10.1007/978-3-031-18304-1_9

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