Quantum Ensembling Methods for Healthcare and Life Science
This work addresses data scarcity problems in healthcare and life sciences, but it is exploratory and incremental in applying quantum methods to existing ensemble techniques.
The study tackled the challenge of learning on small data in healthcare and life sciences by testing quantum ensemble models, achieving results on synthetic datasets and gene expression data for predicting patient response to immunotherapy using up to 56 qubits on quantum hardware.
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.