Quantum Variational Algorithms for the Allocation of Resources in a Cloud/Edge Architecture
arxiv(2024)
摘要
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.
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