Quantum Machine Learning and Data Re-Uploading: Evaluation on Benchmark and Laboratory Medicine Data Sets

Quantum machine learning (QML) is an emerging field that may offer unique advantages over classical machine learning but has not been extensively studied with real-world healthcare data of practical size. This study evaluates the performance of a recently proposed quantum machine learning algorithm-quantum circuits with data re-uploading (QC-REUP)-in comparison to classical machine learning and other QML methods. Performance was assessed using both benchmarking data sets and real-world laboratory medicine data.

Four data sets containing between 2 and 30 features were selected for evaluation. Initial baseline comparisons of classification performance (F1 score) were conducted using all algorithms-QC-REUP, 2 QML, and 4 classical machine learning (ML) algorithms-across the four data sets. Configuration parameters were then optimized for the QC-REUP algorithm using a previously published data set of plasma amino acid (PAA) profiles to determine the impact of optimization on classification performance, followed by a final comparison against classical ML algorithms.

Baseline comparisons showed that QC-REUP outperformed quantum and linear classical algorithms on lower-dimensional data sets. However, as input dimensionality increased, QML F -score declined. Following optimization, QC-REUP improved relative to baseline, and again performed comparably to linear algorithms, but ultimately demonstrated lower performance than nonlinear classical ML algorithms on the PAA data set.

This study suggests that data re-uploading algorithms can perform comparably to classical approaches in specific contexts, particularly with low-dimensional data. While optimization can enhance performance, further improvements in quantum hardware and algorithmic development are likely needed before QML can be effectively applied in laboratory medicine and broader biomedical research.