Quantum Ensembling Methods for Healthcare and Life Science
Journal:
arXiv
Published Date:
Jun 2, 2025
Abstract
Learning on small data is a challenge frequently encountered in many
real-world applications. In this work we study how effective quantum ensemble
models are when trained on small data problems in healthcare and life sciences.
We constructed multiple types of quantum ensembles for binary classification
using up to 26 qubits in simulation and 56 qubits on quantum hardware. Our
ensemble designs use minimal trainable parameters but require long-range
connections between qubits. We tested these quantum ensembles on synthetic
datasets and gene expression data from renal cell carcinoma patients with the
task of predicting patient response to immunotherapy. From the performance
observed in simulation and initial hardware experiments, we demonstrate how
quantum embedding structure affects performance and discuss how to extract
informative features and build models that can learn and generalize
effectively. We present these exploratory results in order to assist other
researchers in the design of effective learning on small data using ensembles.
Incorporating quantum computing in these data constrained problems offers hope
for a wide range of studies in healthcare and life sciences where biological
samples are relatively scarce given the feature space to be explored.
