Quantum computing at the frontiers of biological sciences

Computing plays a critical role in the biological sciences but faces increasing challenges of scale and complexity. Quantum computing, a computational paradigm exploiting the unique properties of quantum mechanical analogs of classical bits, seeks to address many of these challenges. We discuss the potential for quantum computing to aid in the merging of insights across different areas of biological sciences.

[1]  Lov K. Grover.  A fast quantum mechanical algorithm for database search , 1996, STOC '96.

[2]  Daniel A. Lidar,et al.  Quantum annealing versus classical machine learning applied to a simplified computational biology problem , 2018, npj Quantum Information.

[3]  Kaanan P. Shah,et al.  A gene-based association method for mapping traits using reference transcriptome data , 2015, Nature Genetics.

[4]  John D. Murray,et al.  Feedforward and feedback frequency-dependent interactions in a large-scale laminar network of the primate cortex , 2016 .

[5]  Stephen M. Smith,et al.  Brain network dynamics are hierarchically organized in time , 2017, Proceedings of the National Academy of Sciences.

[6]  S. B. Needleman,et al.  A general method applicable to the search for similarities in the amino acid sequence of two proteins. , 1970, Journal of molecular biology.

[7]  Kristan Temme,et al.  Supervised learning with quantum-enhanced feature spaces , 2018, Nature.

[8]  Andrew M. Childs,et al.  Quantum Spectral Methods for Differential Equations , 2019, Communications in Mathematical Physics.

[9]  S. Lloyd,et al.  Environment-assisted quantum walks in photosynthetic energy transfer. , 2008, The Journal of chemical physics.

[10]  E. Farhi,et al.  A Quantum Approximate Optimization Algorithm , 2014, 1411.4028.

[11]  Bruce J. Berne,et al.  Global Optimization : Quantum Thermal Annealing with Path Integral Monte Carlo , 1999 .

[12]  Manolis Kellis,et al.  Interpreting non-coding variation in complex disease genetics , 2012, Nature Biotechnology.

[13]  J. Gambetta,et al.  Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets , 2017, Nature.

[14]  G. Rose,et al.  Finding low-energy conformations of lattice protein models by quantum annealing , 2012, Scientific Reports.

[15]  J. Krystal,et al.  Searching for Cross-Diagnostic Convergence: Neural Mechanisms Governing Excitation and Inhibition Balance in Schizophrenia and Autism Spectrum Disorders , 2017, Biological Psychiatry.

[16]  S. Horvath,et al.  A General Framework for Weighted Gene Co-Expression Network Analysis , 2005, Statistical applications in genetics and molecular biology.

[17]  E. Farhi,et al.  A Quantum Adiabatic Evolution Algorithm Applied to Random Instances of an NP-Complete Problem , 2001, Science.

[18]  Seth Lloyd,et al.  Interplay between coherence and decoherence in LHCII photosynthetic complex , 2011, 1106.1986.

[19]  Gian Giacomo Guerreschi,et al.  Motional effects on the efficiency of excitation transfer , 2010, 1002.0346.

[20]  M. Sipser,et al.  Limit on the Speed of Quantum Computation in Determining Parity , 1998, quant-ph/9802045.

[21]  Michael J. McConnell,et al.  Implementation of a Hamming-Distance-Like Genomic Quantum Classifier Using Inner Products on IBMQX4 and IBMQX16 , 2019, 1907.08267.

[22]  Tobias J. Osborne,et al.  A quantum algorithm to solve nonlinear differential equations , 2008, 0812.4423.

[23]  Velimir V. Vesselinov,et al.  Nonnegative/Binary matrix factorization with a D-Wave quantum annealer , 2017, PloS one.

[24]  G. Chan,et al.  The electronic complexity of the ground-state of the FeMo cofactor of nitrogenase as relevant to quantum simulations. , 2018, The Journal of chemical physics.

[25]  N. Logothetis.  What we can do and what we cannot do with fMRI , 2008, Nature.

[26]  Bruce Fischl,et al.  Combined Volumetric and Surface Registration , 2009, IEEE Transactions on Medical Imaging.

[27]  Ronald de Wolf,et al.  A Survey of Quantum Learning Theory , 2017, ArXiv.

[28]  H. Sebastian Seung,et al.  Algorithms for Non-negative Matrix Factorization , 2000, NIPS.

[29]  Walter Vinci,et al.  Quantum variational autoencoder , 2018, Quantum Science and Technology.

[30]  G. Brassard,et al.  Quantum Amplitude Amplification and Estimation , 2000, quant-ph/0005055.

[31]  Tomaso Poggio,et al.  From Understanding Computation to Understanding Neural Circuitry , 1976 .

[32]  Byron Boots,et al.  Learning Hidden Quantum Markov Models , 2017, AISTATS.

[33]  Rasool Tahmasbi,et al.  Comparison of methods that use whole genome data to estimate the heritability and genetic architecture of complex traits , 2017, Nature Genetics.

[34]  R. W. Robinson.  Counting unlabeled acyclic digraphs , 1977 .

[35]  M. Sipser,et al.  Quantum Computation by Adiabatic Evolution , 2000, quant-ph/0001106.

[36]  S. Sankararaman,et al.  A scalable estimator of SNP heritability for Biobank-scale data , 2018, bioRxiv.

[37]  Richard Durbin,et al.  Sequence analysis Fast and accurate short read alignment with Burrows – Wheeler transform , 2009 .

[38]  A. Price,et al.  Dissecting the genetics of complex traits using summary association statistics , 2016, Nature Reviews Genetics.

[39]  Ryan E. Mills,et al.  Intersection of diverse neuronal genomes and neuropsychiatric disease: The Brain Somatic Mosaicism Network , 2017, Science.

[40]  M S Waterman,et al.  Identification of common molecular subsequences. , 1981, Journal of molecular biology.

[41]  Thierry Paul,et al.  Quantum computation and quantum information , 2007, Mathematical Structures in Computer Science.

[42]  Ramesh Hariharan,et al.  String matching in Õ(sqrt(n)+sqrt(m)) quantum time , 2003, J. Discrete Algorithms.

[43]  F. Petruccione,et al.  An introduction to quantum machine learning , 2014, Contemporary Physics.

[44]  Travis S. Humble,et al.  Quantum supremacy using a programmable superconducting processor , 2019, Nature.

[45]  Anne E Carpenter,et al.  Opportunities and obstacles for deep learning in biology and medicine , 2017, bioRxiv.

[46]  P. Visscher,et al.  GCTA: a tool for genome-wide complex trait analysis. , 2011, American journal of human genetics.

[47]  E. Myers,et al.  Basic local alignment search tool. , 1990, Journal of molecular biology.

[48]  Peter Dayan,et al.  Integrated accounts of behavioral and neuroimaging data using flexible recurrent neural network models , 2018, bioRxiv.

[49]  Masoud Mohseni,et al.  Commercialize quantum technologies in five years , 2017, Nature.

[50]  Roger Melko,et al.  Quantum Boltzmann Machine , 2016, 1601.02036.

[51]  J. Marchini,et al.  Genome-wide association studies of brain imaging phenotypes in UK Biobank , 2018, Nature.

[52]  Seth Lloyd,et al.  Quantum algorithm for data fitting. , 2012, Physical review letters.

[53]  Ashley Montanaro,et al.  Quantum Pattern Matching Fast on Average , 2014, Algorithmica.

[54]  A. Harrow,et al.  Quantum algorithm for linear systems of equations. , 2008, Physical review letters.

[55]  Vlatko Vedral,et al.  Towards quantum simulations of biological information flow , 2012, Interface Focus.

[56]  M. Schuld,et al.  Circuit-centric quantum classifiers , 2018, Physical Review A.

[57]  Sarah A Harris,et al.  Quantum-assisted biomolecular modelling , 2010, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[58]  John H Krystal,et al.  Functional hierarchy underlies preferential connectivity disturbances in schizophrenia , 2015, Proceedings of the National Academy of Sciences.

[59]  T. Ge,et al.  Resting brain dynamics at different timescales capture distinct aspects of human behavior , 2019, Nature Communications.

[60]  John D. Murray,et al.  Hierarchical Heterogeneity across Human Cortex Shapes Large-Scale Neural Dynamics , 2019, Neuron.

[61]  Animesh Datta,et al.  Entanglement and entangling power of the dynamics in light-harvesting complexes , 2009, 0912.0122.

[62]  Alkes L. Price,et al.  Integrative approaches for large-scale transcriptome-wide association studies , 2015 .

[63]  Michael G. Raymer,et al.  The U.S. National Quantum Initiative: From Act to action , 2019, Science.

[64]  R. Durbin,et al.  Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses , 2012, Nature Protocols.

[65]  Maurizio Corbetta,et al.  Resting-State Functional Connectivity Emerges from Structurally and Dynamically Shaped Slow Linear Fluctuations , 2013, The Journal of Neuroscience.

[66]  Prashant S. Emani,et al.  Comprehensive functional genomic resource and integrative model for the human brain , 2018, Science.

[67]  Maria Schuld,et al.  Quantum Machine Learning in Feature Hilbert Spaces. , 2018, Physical review letters.

[68]  Richard Durbin,et al.  Fast and accurate long-read alignment with Burrows–Wheeler transform , 2010, Bioinform..

[69]  Thomas R. Gingeras,et al.  STAR: ultrafast universal RNA-seq aligner , 2013, Bioinform..

[70]  Andrew M. Childs,et al.  Quantum Algorithm for Linear Differential Equations with Exponentially Improved Dependence on Precision , 2017, Communications in Mathematical Physics.

[71]  Oliver Y. Chén,et al.  The human cortex possesses a reconfigurable dynamic network architecture that is disrupted in psychosis , 2018, Nature Communications.

[72]  Andris Ambainis,et al.  Quantum algorithm for tree size estimation, with applications to backtracking and 2-player games , 2017, STOC.

[73]  K. Wiesner,et al.  Hidden Quantum Markov Models and non-adaptive read-out of many-body states , 2010, 1002.2337.

[74]  Óscar Promio Muñoz.  Quantum Annealing in the transverse Ising Model , 2018 .

[75]  P. Donnelly,et al.  A Flexible and Accurate Genotype Imputation Method for the Next Generation of Genome-Wide Association Studies , 2009, PLoS genetics.

[76]  Matthew D. Rasmussen,et al.  Genome-Wide Inference of Ancestral Recombination Graphs , 2013, PLoS genetics.

[77]  R. Durbin,et al.  Inference of human population history from individual whole-genome sequences. , 2011, Nature.

[78]  R. Griffiths,et al.  An ancestral recombination graph , 1997 .

[79]  B. T. Thomas Yeo,et al.  Interpreting temporal fluctuations in resting-state functional connectivity MRI , 2017, NeuroImage.

[80]  Animesh Datta,et al.  Noise-assisted energy transfer in quantum networks and light-harvesting complexes , 2009, 0910.4153.

[81]  M. Corbetta,et al.  How Local Excitation–Inhibition Ratio Impacts the Whole Brain Dynamics , 2014, The Journal of Neuroscience.

[82]  F. Nori,et al.  Quantum biology , 2012, Nature Physics.

[83]  Maria Schuld,et al.  Implementing a distance-based classifier with a quantum interference circuit , 2017, 1703.10793.

[84]  R. Durbin,et al.  Mapping trait loci by use of inferred ancestral recombination graphs. , 2006, American journal of human genetics.

[85]  J. Doll,et al.  Quantum annealing: A new method for minimizing multidimensional functions , 1994, chem-ph/9404003.

[86]  Seth Lloyd,et al.  Quantum random access memory. , 2007, Physical review letters.

[87]  H. Stapp.  The importance of quantum decoherence in brain processes , 2000, quant-ph/0010029.

[88]  Shane A. McCarthy,et al.  Reference-based phasing using the Haplotype Reference Consortium panel , 2016, Nature Genetics.