Pith Number

pith:ZTS5JMRW

pith:2026:ZTS5JMRWW4S23ET673AF24SQMY

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Robust Mutation Analysis of Quantum Programs Under Noise

E\~naut Mendiluze Usandizaga, Mohammad Reza Mousavi, Paolo Arcaini, Shaukat Ali, Sophie Fortz

Noise alters behavioral distances between quantum programs and mutants, requiring noise-specific thresholds for detection.

arxiv:2605.13279 v1 · 2026-05-13 · cs.SE

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Claims

C1strongest claim

Our results show that noise significantly alters the behavioral distance between programs and mutants, making equivalent mutants harder to distinguish from real faults. Density-matrix metrics achieve the best discrimination, with misclassification rates up to 16.77%, but output-distribution metrics reach up to 73.03% accuracy and 74.89% F1-score. Noise-specific thresholds further improve detection compared to noiseless thresholds.

C2weakest assumption

That the three IBM-device noise profiles emulated in the simulators accurately represent real hardware behavior and that the 41 chosen programs plus mutation operators are representative of broader quantum software.

C3one line summary

Noise from quantum hardware simulators significantly alters mutant detection distances, making equivalent mutants harder to separate from faults, with output-distribution metrics reaching 73.03% accuracy and 74.89% F1-score under device-specific thresholds.

References

84 extracted · 84 resolved · 2 Pith anchors

[1] Konstantinos Adamopoulos, Mark Harman, and Robert M. Hierons. 2004. How to Overcome the Equivalent Mutant Problem and Achieve Tailored Selective Mutation Using Co-evolution. InGenetic and Evolutionary 2004 · doi:10.1007/978-3-540-24855-2_155

[2] Shaukat Ali, Paolo Arcaini, Xinyi Wang, and Tao Yue. 2021. Assessing the Effectiveness of Input and Output Coverage Criteria for Testing Quantum Programs. In14th IEEE Conference on Software Testing, V 2021 · doi:10.1109/icst49551.2021

[3] R Alicki. 2004. Decoherence and the appearance of a classical world in quantum theory.Journal of Physics A: Mathematical and General37, 5 (2004), 1948–1949 2004

[4] James H. Andrews, Lionel C. Briand, Yvan Labiche, and Akbar Siami Namin. 2006. Using Mutation Analysis for Assessing and Comparing Testing Coverage Criteria.IEEE Trans. Software Eng.32, 8 (2006), 608– 2006 · doi:10.1109/tse.2006.83

[5] Andrea Arcuri and Lionel C. Briand. 2014. A Hitchhiker’s guide to statistical tests for assessing randomized algorithms in software engineering.Softw. Test. Verification Reliab.24, 3 (2014), 219–250. 2014 · doi:10.1002/stvr.1486

Receipt and verification

First computed	2026-05-18T02:44:49.211972Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

cce5d4b236b725ad927efec05d725066395d3abc802baaddec1246fa1d98eb48

Aliases

arxiv: 2605.13279 · arxiv_version: 2605.13279v1 · doi: 10.48550/arxiv.2605.13279 · pith_short_12: ZTS5JMRWW4S2 · pith_short_16: ZTS5JMRWW4S23ET6 · pith_short_8: ZTS5JMRW

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/ZTS5JMRWW4S23ET673AF24SQMY \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: cce5d4b236b725ad927efec05d725066395d3abc802baaddec1246fa1d98eb48

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "e377cc16ac91f4e45d231c8b66861d97dc20219676bce6ee447d27af64f312ff",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.SE",
    "submitted_at": "2026-05-13T09:56:05Z",
    "title_canon_sha256": "5db7a24b6bbfc71267bae73387f5a2a0bb54dc42fe8ac429dce93d8f1012ef2e"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.13279",
    "kind": "arxiv",
    "version": 1
  }
}