{
  "id": 13730,
  "url": "https://arxiv.org/abs/2607.21186v1",
  "title": "Do emulated quantum circuits change what CNNs look at? Performance and explainability comparison in medical image classification",
  "summary": "Numerous studies have analyzed the use of hybrid quantum-classical convolutional neural networks as a promising alternative to classical deep learning. However, network components on quantum hardware impose fundamental limitations, while the scalability of quantum circuits leads to trainability issues. In this work, we investigate whether small, classically-emulated quantum circuit components can play a meaningful role within complex models, offering an alternative to purely classical convolutio",
  "authors": "Guillermo Rubiños Rodríguez, Martín Ottavianelli, Mateo Alonso, Gonzalo Blázquez Gil, Boris-Stephan Rauchmann, Pablo Díez-Valle, Sergio Altares-López",
  "category": "research",
  "topics": "healthcare,transparency,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-23T11:15:35.000Z",
  "fetched_at": "2026-07-27T05:10:06.638Z",
  "source_slug": "x-arxiv-fairness-query",
  "source_name": "arXiv fairness query",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/13730",
  "original_url": "https://arxiv.org/abs/2607.21186v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}