Quantum computing at the frontiers of biological sciences
The potential for quantum computing to aid in the merging of insights across different areas of biological sciences is discussed.
Abstract
The search for meaningful structure in biological data has relied on\ncutting-edge advances in computational technology and data science methods.\nHowever, challenges arise as we push the limits of scale and complexity in\nbiological problems. Innovation in massively parallel, classical computing\nhardware and algorithms continues to address many of these challenges, but\nthere is a need to simultaneously consider new paradigms to circumvent current\nbarriers to processing speed. Accordingly, we articulate a view towards quantum\ncomputation and quantum information science, where algorithms have demonstrated\npotential polynomial and exponential computational speedups in certain\napplications, such as machine learning. The maturation of the field of quantum\ncomputing, in hardware and algorithm development, also coincides with the\ngrowth of several collaborative efforts to address questions across length and\ntime scales, and scientific disciplines. We use this coincidence to explore the\npotential for quantum computing to aid in one such endeavor: the merging of\ninsights from genetics, genomics, neuroimaging and behavioral phenotyping. By\nexamining joint opportunities for computational innovation across fields, we\nhighlight the need for a common language between biological data analysis and\nquantum computing. Ultimately, we consider current and future prospects for the\nemployment of quantum computing algorithms in the biological sciences.\n