Drug Trial Design

Designing clinical trials involves optimising patient selection, dosage, and trial protocols under uncertainty, with vast combinatorial possibilities. Classical simulations struggle to efficiently explore all trial design variables, leading to longer, costlier trials and suboptimal results.

Quantum computing, particularly quantum optimisation and quantum machine learning, can efficiently tackle these complex combinatorial problems, improving patient stratification, site selection, and adaptive trial simulations.

This can streamline trials by reducing costs and duration, improving trial success rates, and enabling more precise, personalised treatments to reach patients faster.