Assessing Quantum Computing Performance for Energy Optimization in a Prosumer Community

Mastroianni, Carlo; Plastina, Francesco; Scarcello, Luigi; Settino, Jacopo; Vinci, Andrea

doi:10.1109/tsg.2023.3286106

Cited by 6 publications

(2 citation statements)

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“…Indeed, the average number of iterations grows proportionally to the number of repetitions, as shown in Figure 8. As also shown in a previous work, i.e., [42], noise and decoherence effects increase with the depth of the circuit, a phenomenon that impacts QAOA much more than VQE 5 . When combining the results of two figures, we can see that VQE is definitely preferable, since it ensures much better performances with a much shallower circuit, thanks to its ability to reduce the search space, even with the simplest ansatz.…”

Section: A Comparing Qaoa and Vqesupporting

confidence: 82%

“…This procedure has inspired algorithms that can be run on a gate-based quantum computer, such as those provided by major IT companies. As an example, we employed such an approach in a previous work on the optimization of energy exchanges within a prosumer community [42]. The applications of quantum algorithms to edge computing are all very recent.…”

Section: Related Workmentioning

confidence: 99%

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Variational Quantum Algorithms for the Allocation of Resources in a Cloud/Edge Architecture

Mastroianni,

Plastina,

Settino

et al. 2024

IEEE Trans. Quantum Eng.

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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, show that the VQE algorithm ensures better performances when it is equipped with appropriate circuit 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.INDEX TERMS quantum computing, cloud/edge computing, resource assignment

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Section: A Comparing Qaoa and Vqesupporting

confidence: 82%

Section: Related Workmentioning

confidence: 99%