The growth of the need for quantum computers in many domains such as machine learning, numerical scientific simulation and finance has necessitated that quantum computers produce stable results. However, mitigating the impact of the noise inside each quantum device presents an immediate challenge. In this paper, we investigate the temporal behavior of noisy intermediate-scale quantum (NISQ) computers based on calibration data and the characteristics of individual devices. In particular, we collect calibration data of IBM-Q machines over 90 days and compare the quantum error robustness against the processor types, quantum topology and, quantum volumes of the IBM-Q machines. We compared the quantum error data of four IBM-Q quantum computers during 2019-2021, showing that only one computer experienced significant error growth over time. We test the stationary of the quantum errors’ time serial data and build temporal prediction models that can achieve 80% to 94% of prediction accuracy for T1, T2, and single qubit gate error. We define a new evaluation metric, qubit efficiency, to guide the decision of finding the best-fit quantum machine for a quantum circuit in practice