Predicting the Temporal Variability of Error Rates in Superconducting Quantum Processors

Multiple layers of the quantum system full-stack require data on temporal variations in error rates of physical elements. The absence of this information, especially when users access cloud-based quantum systems with waiting queues between compilation and execution, can lead to unreliable computations due to outdated calibration data. One example is the compilation process that impacts the success rate of quantum circuits. This paper proposes using five machine learning and time-series models to predict error rate variability for two-qubit gates based on calibration data. We compare model accuracy and complexity in terms of execution time. The predicted information is applied to a noise-aware compiler to evaluate the impact on success rate. Experimental results on a real superconducting quantum processor demonstrate accurate predictions of temporal error rate variability, achieving performance comparable to that of real calibration data, reducing the impact of queue waiting times that can reach up to two days.