Quantum-enhanced optimization for patient stratification in clinical trials

TL;DR

This paper presents a quantum-enhanced, optimization-based approach to patient stratification that minimizes covariate imbalance across numerical and categorical variables prior to outcome assessment. It employs a hybrid quantum-classical framework that accelerates solution of the combinatorial allocation problem and demonstrates scalability on real trial data. Using three real-world datasets, the method achieves high-quality stratification and up to a 100x reduction in computational time compared with classical solvers, and up to a fivefold increase in statistical significance for treatment effect estimates. These results suggest that optimization-driven stratification can strengthen trial design, improve decision confidence, and reduce costly late-stage failures.

Abstract

Clinical trials are notorious for their high failure rates and steep costs, leading to wasted time and resources spend, prolonged development timelines, and delayed patient access to new therapies. A key contributor to these failures is biological uncertainty, which complicates trial design and weakens the ability to detect true treatment effects. In particular, inadequate patient stratification often results in covariate imbalances across treatment arms, masking treatment effects and reducing statistical power, even when therapies are effective for specific patient subpopulations. This work presents an optimization-based, quantum-enhanced approach to patient stratification that explicitly minimizes covariate imbalance across numerical and categorical variables, without altering protocol design or trial endpoints. Using real clinical trial data, we demonstrate that hybrid quantum-classical optimization methods achieve high-quality stratification while scaling efficiently to larger cohorts. In our benchmark study, the quantum-enhanced pipeline delivered over a 100x improvement in computational efficiency compared to classical approaches, enabling faster iteration and practical deployment at scale. This report shows how improved stratification can lead to decision-relevant gains, including up to a fivefold increase in statistical significance in treatment effect estimation, reducing treatment-effect dilution and increasing trial sensitivity. Together, these results show that optimization-driven stratification can strengthen clinical trial design, improve confidence in downstream decisions, and reduce the risk of costly late-stage failure.

Figures (3)

• Figure 1: Discrepancy (left) and execution time (right) for different optimization approaches on the PBC dataset. The black line indicates the average discrepancy obtained from random stratification.
• Figure 2: Covariate-level balance before and after optimization.
• Figure 3: Evolution of log-rank p-values and normalized discrepancy under targeted patient removal.