Quantum Computing at the Frontiers of Biological Sciences

TL;DR

This paper explores how quantum computing could revolutionize biological data analysis by providing new computational paradigms, highlighting current developments, challenges, and interdisciplinary opportunities in applying quantum algorithms to biology.

Contribution

It presents a comprehensive view of the potential for quantum computing to advance biological sciences, emphasizing interdisciplinary integration and future prospects.

Findings

Quantum algorithms show potential polynomial and exponential speedups.

Collaborative efforts are growing across biology and quantum computing fields.

Quantum computing could enable new insights in genetics, neuroimaging, and behavioral studies.

Abstract

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.