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A quantum generative model for in silico clinical trials using scarce training datasets — arXiv:2608.28168

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.28168
License (as archived): CC0-1.0
Archived: 2026-10-17T11:58:23.119Z
SHA-256 of the archived text: c650f9fe3ecc536e1f9b8dc7430ebd7df15bfc3c70e1030779a45cf43613d39d
Stable URL of this page: https://metraipolis.com/documento/dc_f6ea375dbc8f

How to cite

A quantum generative model for in silico clinical trials using scarce training datasets — arXiv:2608.28168 — arXiv (en), https://arxiv.org/abs/2608.28168 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_f6ea375dbc8f (Metraipolis archive dc_f6ea375dbc8f, sha256 c650f9fe3ecc536e1f9b8dc7430ebd7df15bfc3c70e1030779a45cf43613d39d)

BibTeX

@misc{dcf6ea375dbc8f,
  title = {A quantum generative model for in silico clinical trials using scarce training datasets — arXiv:2608.28168},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.28168},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_f6ea375dbc8f; sha256 c650f9fe3ecc536e1f9b8dc7430ebd7df15bfc3c70e1030779a45cf43613d39d},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.28168. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

In silico methods have emerged as a strategy to complement clinical trials. These are particularly relevant for rare or heterogeneous diseases for which traditional methods are costly or difficult to apply. While classical generative models have shown an extremely good ability to generate high fidelity data when trained using extensive databases, they often struggle when the available samples for training are scarce. In this work, we leverage the potential of quantum computers to represent complex probability distributions to generate high fidelity in silico patients. We propose a pipeline able to combine asymmetric databases into a quantum circuit that serves as a quantum generative model. We evaluate the efficacy of our proposal using a database of Myelodysplastic Syndrome (MDS) patients with 7 clinical variables as a proof-of-concept. We executed our quantum generative model in the IBM Heron r2 ``ibm\_basquecountry'' superconducting quantum computer and compare our method with well known classical baselines. Our results show that the quantum generative model surpasses the classical generative models in generalization and expressivity metrics, indicating its potential validity to generate high fidelity in silico patients for clinical trials.

Tema degli esploratori: fisica e astronomia (retro-catalogo arXiv: annunci del 2026-08-31 in quant-ph).
Categorie arXiv: Quantum Physics (quant-ph).
Pagina del lavoro: https://arxiv.org/abs/2608.28168 — 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 2026-10-17T11:28:37.510Z dalla pagina https://arxiv.org/catchup/quant-ph/2026-08-31?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).