Year: 2025
Type: conference-paper
Abstract: Quantum computing in supercomputing centers requires robust tools to analyze calibration datasets, predict hardware performance, and optimize operational workflows. This paper presents a data-driven framework for processing calibration metrics. Our model is based on a real calibration quality metrics dataset from our in-house$\mathbf{2 0}$-qubit NISQ device and for more than 250 days. We apply detailed data analysis to uncover temporal patterns and cross-metric correlations. Using unsupervised clustering, we identify stable and noisy qubits. We also validate our model using GHZ state experiments. Our study provides health indicators as well as hardware-driven maintenance and recalibration recommendations, thus motivating the integration of relevant schedulers with HPCQC workflows.
Authors: Xiaolong Deng, Laura Schulz, Martin Schulz
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4416875302