Qubit Health Analytics and Clustering for HPC-Integrated Quantum Processors
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866918132510097408 |
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| author | Deng, Xiaolong Schulz, Laura Schulz, Martin |
| author_facet | Deng, Xiaolong Schulz, Laura Schulz, Martin |
| contents | 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 20-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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_21231 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Qubit Health Analytics and Clustering for HPC-Integrated Quantum Processors Deng, Xiaolong Schulz, Laura Schulz, Martin Quantum Physics 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 20-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. |
| title | Qubit Health Analytics and Clustering for HPC-Integrated Quantum Processors |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2508.21231 |