Empowering Quantum Error Traceability with MoE for Automatic Calibration

Quantum computing offers the potential for exponential speedups over classical computing in tackling complex tasks, such as large-number factorization and chemical molecular simulation. However, quantum noise remains a significant challenge, hindering the reliability and scalability of quantum systems. Therefore, effective characterization and calibration of quantum noise are critical to advancing these systems. Quantum calibration is a process that heavily relies on expert knowledge, and there currently is a range of research focused on automatic calibration. However, traditional calibration methods often need an effective error traceback mechanism, leading to repeated calibration attempts without identifying root causes. To address the issue of error traceback in calibration failures, this paper proposes an automatic calibration error traceback algorithm facilitated by a Mixture of Experts (MoE) system inspired by the current large language model technologies. Our approach enables traceability of quantum calibration errors, allowing for the rapid identification and correction of deviations from the calibration state. Extensive experimental results demonstrate that the MoE-based automatic calibration method significantly outperforms traditional error traceability and calibration efficiency techniques. Notably, our approach improved the average visibility of 77 qubits by 25.5%, surpassing the outcomes of fixed calibration processes. This work presents a promising path toward more reliable and scalable quantum computing systems.