QPU integration in OpenCL for heterogeneous programming

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ISSN: 0920-8542
E-ISSN: 1573-0484

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The integration of quantum processing units (QPUs) in a heterogeneous high-performance computing environment requires solutions that facilitate hybrid classical–quantum programming. Standards such as OpenCL facilitate the programming of heterogeneous environments, consisting of CPUs and hardware accelerators. This study presents an innovative method that incorporates QPU functionality into OpenCL, standardizing quantum processes within classical environments. By leveraging QPUs within OpenCL, hybrid quantum–classical computations can be sped up, impacting domains like cryptography, optimization problems, and quantum chemistry simulations. Using Portable Computing Language (Jääskeläinen et al. in Int J Parallel Program 43(5):752–785, 2014. https://doi.org/10.1007/s10766-014-0320-y) and the Qulacs library (Suzuki et al. in Quantum 5:559, 2021. https://doi.org/10.22331/q-2021-10-06-559), results demonstrate, for instance, the successful execution of Shor’s algorithm (Nielsen and Chuang in Quantum computation and quantum information, 10th anniversary edn. Cambridge University Press, Cambridge, 2010), serving as a proof of concept for extending the approach to larger qubit systems and other hybrid quantum–classical algorithms. This integration approach bridges the gap between quantum and classical computing paradigms, paving the way for further optimization and application to a wide range of computational problems.

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Vázquez-Pérez, J., Piñeiro, C., Pichel, J.C. et al. QPU integration in OpenCL for heterogeneous programming. J Supercomput 80, 11682–11703 (2024)

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This work was supported by MICINN through the European Union NextGenerationEU recovery plan (PRTR-C17.I1), and by the Galician Regional Government through the “Planes Complementarios de I+D+I con las Comunidades Autónomas” in Quantum Communication. Simulations on this work were performed using the Finisterrae III Supercomputer, funded by the project CESGA-01 FINISTERRAE III. This work was also supported by the Ministry of Economy and Competitiveness, Government of Spain (Grant Numbers PID2019-104834GB-I00, PID2022-141623NB-I00 and PID2022-137061OB-C22), Consellería de Cultura, Educación e Ordenación Universitaria (accreditations ED431C 2022/16 and ED431G-2019/04), and the European Regional Development Fund (ERDF), which acknowledges the CiTIUS-Research Center in Intelligent Technologies of the University of Santiago de Compostela as a Research Center of the Galician University System.

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Atribución 4.0 Internacional
© The Author(s) 2024