Stochastic Qubit Resource Allocation for Quantum Cloud Computing

• URL: http://arxiv.org/abs/2210.12343v2
• Date: Wed, 11 Jan 2023 08:39:20 GMT
• Title: Stochastic Qubit Resource Allocation for Quantum Cloud Computing
• Authors: Rakpong Kaewpuang, Minrui Xu, Dusit Niyato, Han Yu, Zehui Xiong and Jiawen Kang
• Abstract summary: In quantum cloud computing, quantum cloud providers provision quantum resources in reservation and on-demand plans for users. We propose a quantum resource allocation for the quantum cloud computing system in which quantum resources and the minimum waiting time of quantum circuits are jointly optimized.
• Score: 66.97282014860265
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Quantum cloud computing is a promising paradigm for efficiently provisioning quantum resources (i.e., qubits) to users. In quantum cloud computing, quantum cloud providers provision quantum resources in reservation and on-demand plans for users. Literally, the cost of quantum resources in the reservation plan is expected to be cheaper than the cost of quantum resources in the on-demand plan. However, quantum resources in the reservation plan have to be reserved in advance without information about the requirement of quantum circuits beforehand, and consequently, the resources are insufficient, i.e., under-reservation. Hence, quantum resources in the on-demand plan can be used to compensate for the unsatisfied quantum resources required. To end this, we propose a quantum resource allocation for the quantum cloud computing system in which quantum resources and the minimum waiting time of quantum circuits are jointly optimized. Particularly, the objective is to minimize the total costs of quantum circuits under uncertainties regarding qubit requirement and minimum waiting time of quantum circuits. In experiments, practical circuits of quantum Fourier transform are applied to evaluate the proposed qubit resource allocation. The results illustrate that the proposed qubit resource allocation can achieve the optimal total costs.

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