Quantum Computing in Omics Medicine

(The speaker is also the team leader of Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences. This is a joint seminar with the iTHEMS Biology Group.)

In medical science, the recent explosive development of omics technologies has enabled the measurement not only of bulk data from entire tissues, but also data for individual cells and their spatial location information, and even allowed collection of such information in real-time. Meaningful interpretation of these rich data requires an ability to understand high-order and complex relationships that underpin biological phenomena such as drug response, simulating their dynamics, and selecting the optimal treatment for each patient based on these results. While these data are large-scale and of ultra-high dimensionality, they are also often sparse, with many missing values in the measurements and frequent higher-order interactions among variables, making them hard to handle with conventional statistics. To make further progress, machine learning – especially deep learning – is emerging as one of the promising ways forward. We have developed a method to transform omics data into an image-like representation for analysis with deep learning (DeepInsight) and have successfully used it to predict drug response and to identify original cell types from single-cell RNA-seq data. However, anticipation of the vast amount of medical data being accumulated gives particular urgency to addressing the problems of the time it actually takes to train deep learning models and the complexity of the necessary computational solutions. One possible way to resolve many of these problems is “quantum transcendence”, which is made possible by quantum superposition computation. Among all the different ways to apply quantum computation to medical science, we are particularly interested in quantum deep learning based on optimization and search problems, quantum modeling of single nucleotide detection by observational systems and statistical techniques such as regression analysis by inverse matrix computation and eigenvalue computation. In this seminar, I will first present an overview of how quantum machine learning and quantum deep learning can be used to formulate treatment strategies in medicine. We will discuss how to implement the quantum DeepInsight method, the challenges of noise in quantum computation when training QCNNs, feature mapping issues, problems of pretraining in quantum deep learning, and concerns relating to handling sensitive data such as genomic sequences. I hope this seminar will enhance our understanding of how to effectively facilitate medical research with quantum computing.