{"paper":{"title":"Qubit Health Analytics and Clustering for HPC-Integrated Quantum Processors","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Laura Schulz, Martin Schulz, Xiaolong Deng","submitted_at":"2025-08-28T21:48:53Z","abstract_excerpt":"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 stu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.21231","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2508.21231/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}