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pith:222VDYXG

pith:2025:222VDYXG532ZBBBV7ZFC3FOREA

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QSMOTE-PGM/kPGM: QSMOTE Based PGM and kPGM for Imbalanced Dataset Classification

Bikash K. Behera, Giuseppe Sergioli, Roberto Giuntini

Quantum-inspired oversampling paired with pretty good measurement classifiers raises recall on imbalanced churn data over random forest.

arxiv:2512.16960 v2 · 2025-12-18 · cs.LG · quant-ph

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Claims

C1strongest claim

Experimental evaluations on the Telco Customer Churn dataset demonstrate that the proposed quantum-inspired approaches consistently outperform a classical Random Forest baseline, particularly in terms of recall and balanced F1-score. Among all configurations, PGM with stereo encoding and n_copies=2 achieves the best performance with an accuracy of 0.8512 and an F1-score of 0.8234.

C2weakest assumption

That the quantum-inspired similarity measures underlying the three QSMOTE variants (KNN-based, Fidelity-based, Margin-based) genuinely improve minority-class representation in a way that generalizes beyond the specific encodings and dataset tested, without post-hoc tuning or encoding-specific artifacts.

C3one line summary

QSMOTE variants with PGM and KPGM classifiers outperform Random Forest on imbalanced Telco churn data, reaching 0.8512 accuracy and 0.8234 F1 using stereo encoding with two quantum copies.

References

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[1] An introduction to quantum machine learning, 2015 · doi:10.1080/00107514

[2] J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and S. Lloyd, “Quantum machine learning,”Nature, vol. 549, no. 7671, pp. 195–202, 2017 2017

[3] P. Hausladen and W. K. Wootters, “Pretty good measurement,”Journal of Modern Optics, vol. 41, no. 12, pp. 2385–2390, 1994 1994

[4] A new quantum approach to binary classification, 2019

[5] Quantum-inspired algorithm for direct multi-class classification, 2023 · doi:10.1016/j.asoc.2022.109956

Receipt and verification

First computed	2026-05-18T03:10:11.526856Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

d6b551e2e6eef5908435fe4a2d95d120005de4f162afdafbb7cb16d6b82c7772

Aliases

arxiv: 2512.16960 · arxiv_version: 2512.16960v2 · doi: 10.48550/arxiv.2512.16960 · pith_short_12: 222VDYXG532Z · pith_short_16: 222VDYXG532ZBBBV · pith_short_8: 222VDYXG

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/222VDYXG532ZBBBV7ZFC3FOREA \
  | 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: d6b551e2e6eef5908435fe4a2d95d120005de4f162afdafbb7cb16d6b82c7772

Canonical record JSON

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    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2025-12-18T07:36:26Z",
    "title_canon_sha256": "e644eab5ec96bf569f3f56902d024bd7253cb00298cac6bbe48c721d128aba25"
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