AI-Driven Resource Optimization of Quantum Service Computing

Abstract

Quantum computing is an emerging technology that is increasingly accessed through cloud service platforms. However, the growing demand for quantum computing is constrained by the limited availability of resources, resulting in long waiting times for users and high execution costs, as well as under-utilization of quantum resources. To address this under-utilization, previous research has demonstrated that quantum task scheduling can effectively optimize the usage of quantum cloud platforms, combining multiple quantum circuits into a single execution task. This paper proposes an optimization method, QCRAFT AI Scheduler, to schedule quantum circuits making use of Artificial Intelligence algorithms. Specifically, this proposal is based on a Deep Learning model, which is capable of predicting the optimal scheduling of task queues, in order to use the maximum number of qubits of quantum computers. The validation results show that the proposal achieves 97.28% resource utilization in the scheduling iterations and reduces the average execution cost by 95.06%.