تقرير
Variational Quantum Algorithms for the Allocation of Resources in a Cloud/Edge Architecture
| العنوان: | Variational Quantum Algorithms for the Allocation of Resources in a Cloud/Edge Architecture |
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| المؤلفون: | Mastroianni, Carlo, Plastina, Francesco, Settino, Jacopo, Vinci, Andrea |
| المصدر: | IEEE Transactions on Quantum Engineering,2024 |
| سنة النشر: | 2024 |
| المجموعة: | Condensed Matter Quantum Physics |
| مصطلحات موضوعية: | Quantum Physics, Condensed Matter - Disordered Systems and Neural Networks, Condensed Matter - Other Condensed Matter |
| الوصف: | Modern Cloud/Edge architectures need to orchestrate multiple layers of heterogeneous computing nodes, including pervasive sensors/actuators, distributed Edge/Fog nodes, centralized data centers and quantum devices. The optimal assignment and scheduling of computation on the different nodes is a very difficult problem, with NP-hard complexity. In this paper, we explore the possibility of solving this problem with Variational Quantum Algorithms, which can become a viable alternative to classical algorithms in the near future. In particular, we compare the performances, in terms of success probability, of two algorithms, i.e., Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE). The simulation experiments, performed for a set of simple problems, %CM230124 that involve a Cloud and two Edge nodes, show that the VQE algorithm ensures better performances when it is equipped with appropriate circuit \textit{ansatzes} that are able to restrict the search space. Moreover, experiments executed on real quantum hardware show that the execution time, when increasing the size of the problem, grows much more slowly than the trend obtained with classical computation, which is known to be exponential. Comment: 14 pages, 13 figures |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1109/TQE.2024.3398410 |
| URL الوصول: | http://arxiv.org/abs/2401.14339 |
| رقم الأكسشن: | edsarx.2401.14339 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1109/TQE.2024.3398410 |
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