  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.
