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.
Highlights
In an era of increasingly collaborative efforts towards unravelling the complexities of biology, one may posit the existence of two broad tendencies: first, an approach towards greater depth in particular fields, whether relying on intensive technological, theoretical or computational development, that aims to comprehensively explore a specific aspect of biology; and second, a recognition of the need to knit together the disparate experimental and conceptual threads across the vast spectrum of length, time and system-size scales inherent in biology into a coherent framework
Given the largedimensional parameter search space for the classification problem, classical computation frequently runs into search efficiency issues
We explore problems whose quantum computing (QC) solutions may depend on the availability and storage in memory of superpositions of qubits
Summary
Emani1,2*, Jonathan Warrell1,2*, Alan Anticevic[3], Stefan Bekiranov[4], Michael. Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, USA. Of Virginia School of Medicine, Charlottesville, Virginia, USA. Canadian Institute for Advanced Research (CIFAR), Toronto, Ontario, Canada. CIFAR Artificial Intelligence Research Chair, Vector Institute, Toronto, Ontario, Canada. Schizophrenia and Bipolar Disorder Program, McLean Hospital, Belmont, Massachusetts, USA. Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK. Joint Center for Quantum Information and Computer Science, University of Maryland, College
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