Abstract: Matching eligible patients to appropriate clinical trials remains a major challenge in healthcare due to large, heterogeneous datasets and complex eligibility criteria. This paper proposes a novel Quantum Machine Learning (QML) algorithm integrated with Natural Language Processing (NLP) techniques to optimize clinical trial matching. Unlike traditional methods, the proposed framework introduces a hybrid quantum–classical pseudocode workflow that combines quantum feature mapping with NLP-based data extraction to improve accuracy and efficiency in patient–trial matching. To strengthen the foundation of the proposed approach, key patented technologies, such as US20200005906A1 (intelligent patient–trial data matching) and US20220068443A1 (NLP-based eligibility interpretation), have been considered, demonstrating the growing feasibility of automated matching systems. The novelty of our work lies in unifying these advances within a scalable QML–NLP framework that reduces manual workload, shortens trial recruitment times, and improves overall research efficiency. By integrating quantum computing with NLP, this study moves beyond existing reviews and provides a concrete, implementable methodology to accelerate patient recruitment and advance personalized medicine. discussion: By leveraging the computational power of quantum computing, current limitations in managing big data for clinical trials can be overcome. The synergy of quantum machine learning and NLP enhances trial efficiency, reduces costs, shortens trial durations, and stabilizes research methodologies.
Arora et al. (2026) studied this question.