A Perspective on Protein Structure Prediction Using Quantum Computers.

Despite the recent advancements by deep learning methods such as AlphaFold2, in silico protein structure prediction remains a challenging problem in biomedical research. With the rapid evolution of quantum computing, it is natural to ask whether quantum computers can offer some meaningful benefits for approaching this problem. Yet, identifying specific problem instances amenable to quantum advantage and estimating the quantum resources required are equally challenging tasks. Here, we share our perspective on how to create a framework for systematically selecting protein structure prediction problems that are amenable for quantum advantage, and estimate quantum resources for such problems on a utility-scale quantum computer. As a proof-of-concept, we validate our problem selection framework by accurately predicting the structure of a catalytic loop of the Zika Virus NS3 Helicase, on quantum hardware.

How can quantum computing be applied in clinical trial design and optimization?

Clinical trials are necessary for assessing the safety and efficacy of treatments. However, trial timelines are severely delayed with minimal success due to a multitude of factors, including imperfect trial site selection, cohort recruitment challenges, lack of efficacy, absence of reliable biomarkers, etc. Each of these factors possesses a unique computational challenge, such as data management, trial simulations, statistical analyses, and trial optimization. Recent advancements in quantum computing offer a promising opportunity to overcome these hurdles. In this opinion we uniquely explore the application of quantum optimization and quantum machine learning (QML) to the design and execution of clinical trials. We examine the current capabilities and limitations of quantum computing and outline its potential to streamline clinical trials.