Authors:
Marie Salm
;
Johanna Barzen
;
Frank Leymann
and
Philipp Wundrack
Affiliation:
University of Stuttgart, Institute of Architecture of Application Systems, Universitätsstraße 38, Stuttgart, Germany
Keyword(s):
Quantum Computing, NISQ Analyzer, Decision Support, Machine Learning, Prediction.
Abstract:
Quantum computers might solve specific problems faster than classical computers in the future. But their actual qubit numbers are small, and the error rates are high. However, quantum computers are already used in various areas and a steadily increasing number is made available by cloud providers. To execute a quantum circuit, it is mapped to the quantum computer’s hardware. The resulting compiled circuit strongly influences the precision of the execution in terms of occurring errors caused by used qubits and quantum gates. Selecting an optimal one is, therefore, essential. SDKs are used to implement circuits and differ in supported cloud providers and programming languages. These differences complicate a change to other backends. In previous work, we developed an automated framework to translate a given circuit and compile it on available quantum computers using multiple compilers. The compilation results can be prioritized and executed. Nevertheless, the translation and compilation
with all compilers and quantum computers is resource-intensive and does not scale well with further backends in the future. We, therefore, present an extension to automatically select suitable compiler and quantum computer combinations based on the user’s needs, e.g., for short waiting times and precise results based on past executions. To demonstrate and validate our approach, we show a prototype and case study.
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