A quantum generative model for in silico clinical trials using scarce training datasets
Abstract
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.
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BibTeX
@misc{larrarte2026quantum,
title = {{A quantum generative model for in silico clinical trials using scarce training datasets}},
author = {Olatz Sanz Larrarte and Reza Dastbasteh and Roberto Sanchez-Navarro and Maria Diez-Campelo and Felipe Prosper and Ana Alfonso-Pierola and Mikel Hernaez and Sara Capponi and Pedro Crespo Bofill and Josu Etxezarreta Martinez},
howpublished = {arXiv},
year = {2026},
month = aug,
doi = {10.48550/arxiv.2608.28168},
eprint = {2608.28168},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2608.28168},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - A quantum generative model for in silico clinical trials using scarce training datasets
AU - Olatz Sanz Larrarte
AU - Reza Dastbasteh
AU - Roberto Sanchez-Navarro
AU - Maria Diez-Campelo
AU - Felipe Prosper
AU - Ana Alfonso-Pierola
AU - Mikel Hernaez
AU - Sara Capponi
AU - Pedro Crespo Bofill
AU - Josu Etxezarreta Martinez
PY - 2026
DA - 2026/08/28
PB - arXiv
DO - 10.48550/arxiv.2608.28168
UR - https://arxiv.org/abs/2608.28168
AB - 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.
ER -