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Object Detection Using Quantum Transfer Learning — arXiv:2609.15394

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.15394
License (as archived): CC0-1.0
Archived: 2026-10-16T02:24:37.498Z
SHA-256 of the archived text: 1c28c76ee7d96ba9bde1fa4b9907887a491a563765b9da50964c0acac11c8eeb
Stable URL of this page: https://metraipolis.com/documento/dc_4ed2ba2ed8fc

How to cite

Object Detection Using Quantum Transfer Learning — arXiv:2609.15394 — arXiv (en), https://arxiv.org/abs/2609.15394 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_4ed2ba2ed8fc (Metraipolis archive dc_4ed2ba2ed8fc, sha256 1c28c76ee7d96ba9bde1fa4b9907887a491a563765b9da50964c0acac11c8eeb)

BibTeX

@misc{dc4ed2ba2ed8fc,
  title = {Object Detection Using Quantum Transfer Learning — arXiv:2609.15394},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.15394},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_4ed2ba2ed8fc; sha256 1c28c76ee7d96ba9bde1fa4b9907887a491a563765b9da50964c0acac11c8eeb},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.15394. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Object detection requires the joint learning of semantic identity and continuous spatial geometry. Existing quantum transfer learning approaches have focused mainly on classification tasks and do not support regression. Here, we introduce a physics informed hybrid classical-quantum architecture for object detection in which a pre-trained MobileNetV2 maps visual features into an eight qubits variationally quantum Hilbert space. The Hilbert space is partitioned into two task specific subspaces for semantic classification and geometric localization. Local quantum expectation values are used to predict continuous bounding box coordinates, while circuit topology regulates the flow of quantum information between the two tasks. We compare linear and circular CNOT topologies and show that periodic boundary conditions suppress bipartite entanglement without degrading detection performance. The circular architecture reaches a bipartite von Neumann entropy of 0.1688 bits and a Meyer-Wallach entanglement measure of 0.0370, while achieving an [email protected] of 0.9448 and an [email protected]:0.95 of 0.6558. A thermodynamic formulation further interprets the total learning loss as a variation in Helmholtz free energy. It interprets geometric localization as an effective internal energy contribution and semantic classification as an entropic contribution. These results indicate that high performance in the proposed object detection model does not require maximal entanglement. Instead, efficient learning can emerge through topology controlled information flow, targeted restriction of entanglement, and information renormalization within the Hilbert space.

Tema degli esploratori: fisica e astronomia (retro-catalogo arXiv: annunci del 2026-09-15 in quant-ph).
Categorie arXiv: Quantum Physics (quant-ph).
Pagina del lavoro: https://arxiv.org/abs/2609.15394 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T01:57:57.083Z dalla pagina https://arxiv.org/catchup/quant-ph/2026-09-15?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).