TY - JOUR T1 - Quantum Computing at the Frontiers of Biological Sciences Y1 - 2019 A1 - Prashant S. Emani A1 - Jonathan Warrell A1 - Alan Anticevic A1 - Stefan Bekiranov A1 - Michael Gandal A1 - Michael J. McConnell A1 - Guillermo Sapiro A1 - Alán Aspuru-Guzik A1 - Justin Baker A1 - Matteo Bastiani A1 - Patrick McClure A1 - John Murray A1 - Stamatios N Sotiropoulos A1 - J. M. Taylor A1 - Geetha Senthil A1 - Thomas Lehner A1 - Mark B. Gerstein A1 - Aram W. Harrow AB -

The search for meaningful structure in biological data has relied on cutting-edge advances in computational technology and data science methods. However, challenges arise as we push the limits of scale and complexity in biological problems. Innovation in massively parallel, classical computing hardware and algorithms continues to address many of these challenges, but there is a need to simultaneously consider new paradigms to circumvent current barriers to processing speed. Accordingly, we articulate a view towards quantum computation and quantum information science, where algorithms have demonstrated potential polynomial and exponential computational speedups in certain applications, such as machine learning. The maturation of the field of quantum computing, in hardware and algorithm development, also coincides with the growth of several collaborative efforts to address questions across length and time scales, and scientific disciplines. We use this coincidence to explore the potential for quantum computing to aid in one such endeavor: the merging of insights from genetics, genomics, neuroimaging and behavioral phenotyping. By examining joint opportunities for computational innovation across fields, we highlight the need for a common language between biological data analysis and quantum computing. Ultimately, we consider current and future prospects for the employment of quantum computing algorithms in the biological sciences. 

UR - https://arxiv.org/abs/1911.07127 ER -