{
  "abstract": "Quantum kernel methods are leading candidates for a practical quantum advantage in machine learning, but assessing that potential requires two quantities usually reported separately: how well a kernel performs on the task, and how far its geometry departs from the classical kernels available for the same problem. We introduce a fully unsupervised, multi-objective protocol that optimises simultaneously the normalised pseudo discrepancy (NPD), a label-free proxy for anomaly detection quality, and the geometric difference (GD) to a tuned classical reference kernel, selecting models from the resulting Pareto front. We apply it to malware beaconing detection in real network traffic, using a one-class support vector machine with fidelity and projected quantum kernels over four data encodings, on simulators and on IQM's 20-qubit Garnet processor. NPD-guided selection alone finds a fidelity kernel that beats the tuned classical baseline, but with a geometric difference too small to certify the gain as quantum. Projected kernels reach far larger geometric differences; the Pareto-selected one only marginally exceeds the baseline (AUC $0.782$ versus $0.765$, $g_{C\\to Q}\\approx 89>\\sqrt{N}$ relative to that reference kernel), still below the NPD-selected fidelity kernel ($0.840$).",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.09717",
  "author_total": 11,
  "authors": [
    {
      "name": "Boaz Micah"
    },
    {
      "name": "Nadia Milazzo"
    },
    {
      "name": "Maissa Beji"
    },
    {
      "name": "Borja Aizpurua"
    },
    {
      "name": "Llorenç Espinosa-Portalés"
    },
    {
      "name": "Esteban Payares"
    },
    {
      "name": "Ghada Ben Slama"
    },
    {
      "name": "Luc Andrea"
    },
    {
      "name": "Michel Kurek"
    },
    {
      "name": "Thomas Cope"
    },
    {
      "name": "Olivier Salomon"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4bc5bq5rh4yfd.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.09717",
  "fulltext_url": null,
  "id": "4bc5bq5rh4yfd",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.09717",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.CR"
    ],
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "quant-ph"
    ],
    [
      "openalex",
      "1702|Quantum Computing Algorithms and Architecture"
    ]
  ],
  "openalex": "W7220892726",
  "pdf_url": "https://arxiv.org/pdf/2610.09717",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-07",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4bc5bq5rh4yfd.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:37Z",
      "native_id": "2610.09717",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.09717"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Pareto-optimal quantum kernel selection for unsupervised anomaly detection on real malware beaconing data",
  "title_text": "Pareto-optimal quantum kernel selection for unsupervised anomaly detection on real malware beaconing data",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:37Z",
  "url": "https://research.draconitic.com/paper/4bc5bq5rh4yfd",
  "venue": "arXiv",
  "versions": []
}