This paper points out the problems such as complexity arising from the digitalization process of medical data, massive data generation, and the need for personalized treatment plans, and suggests that quantum computing (QC) and quantum machine learning (QML) can bring about innovative developments in solving these problems. QC can lead to medical innovation by enabling faster and more accurate diagnosis, personalized treatment, and improved new drug development processes. However, there are difficulties such as algorithm errors and high costs in the process of integrating quantum technology into precision medicine. In this paper, we argue that the reliability and accuracy of quantum computing can be improved through formal methods. Formal methods provide a mathematical framework to precisely perform the specification, development, and verification of quantum algorithms. In particular, in the field of genomic data analysis, we use formal specification languages to define the behavior and properties of quantum algorithms that identify disease-related genetic markers, systematically explore all possible states through model verification tools to verify the accuracy of the algorithm, mathematically prove that the algorithm satisfies the specified properties through theorem proof techniques, and show that the efficiency and performance of quantum algorithms can be improved through formal optimization techniques.
