Abstract
Fascinating progress in understanding our world at the smallest scales moves us to the border of a new technological revolution governed by quantum physics. By taking advantage of quantum phenomena, quantum computing devices allow a speedup in solving diverse tasks. In this Perspective, we discuss the potential impact of quantum computing on computational biology. Bearing in mind the limitations of existing quantum computing devices, we attempt to indicate promising directions for further research in the emerging area of quantum computational biology.
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References
Moore, G. E. Cramming more components onto integrated circuits. IEEE Solid State Circuits Mag. 11, 33–35 (2006).
Waldrop, M. M. The chips are down for Moore’s law. Nature 530, 144–147 (2016).
Markov, I. L. Limits on fundamental limits to computation. Nature 512, 147–154 (2014).
Manin, Y. I. Computable and Noncomputable (in Russian) (Sov. Radio, 1980).
Feynman, R. P. Simulating physics with computers. Int. J. Theor. Phys. 21, 467–488 (1982).
Feynman, R. P. Quantum mechanical computers. Found. Phys. 16, 507–531 (1986).
Ladd, T. D. et al. Quantum computers. Nature 464, 45–53 (2010).
Preskill, J. Quantum computing in the NISQ era and beyond. Quantum 2, 79 (2018).
Shor, P. W. Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer. SIAM J. Comput. 26, 1484–1509 (1997).
Aaronson, S. & Arkhipov, A. The computational complexity of linear optics. Theor. Comput. 9, 143–252 (2013).
Montanaro, A. Quantum algorithms: an overview. npj Quantum Inf. 2, 15023 (2016).
Harrow, A. W. & Montanaro, A. Quantum computational supremacy. Nature 549, 203–209 (2017).
Arute, F. et al. Quantum supremacy using a programmable superconducting processor. Nature 574, 505–510 (2019).
Pednault, E., Gunnels, J. A., Nannicini, G., Horesh, L. & Wisnieff, R. Leveraging secondary storage to simulate deep 54-qubit sycamore circuits. Preprint at https://arxiv.org/abs/1910.09534 (2019).
Huang, C. et al. Classical simulation of quantum supremacy circuits. Preprint at https://arxiv.org/abs/2005.06787 (2020).
Zlokapa, A., Boixo, S. & Lidar, D. Boundaries of quantum supremacy via random circuit sampling. Preprint at https://arxiv.org/abs/2005.02464 (2020).
Zhong, Han-Sen et al. Quantum computational advantage using photons. Science 370, 1460–1463 (2020).
Huh, J., Guerreschi, G. G., Peropadre, B., McClean, J. R. & Aspuru-Guzik, A. Boson sampling for molecular vibronic spectra. Nat. Photon. 9, 615–620 (2015).
Schuld, M., Brádler, K., Israel, R., Su, D. & Gupt, B. Measuring the similarity of graphs with a Gaussian boson sampler. Phys. Rev. A 101, 032314 (2020).
Outeiral, C. et al. The prospects of quantum computing in computational molecular biology. WIREs Comput. Mol. Sci. 11, e1481 (2021).
Lambert, N. et al. Quantum biology. Nat. Phys. 9, 10–18 (2013).
Wang, B.-X. et al. Efficient quantum simulation of photosynthetic light harvesting. npj Quantum Inf. 4, 52 (2018).
Emani, P. S. et al. Quantum computing at the frontiers of biological sciences. Nat. Methods https://doi.org/10.1038/s41592-020-01004-3 (2021).
Preskill, J. in Introduction to Quantum Computation and Information 213–269 (World Scientific, 1998).
Albash, T. & Lidar, D. A. Adiabatic quantum computation. Rev. Mod. Phys. 90, 015002 (2018).
Aharonov, D. et al. Adiabatic quantum computation is equivalent to standard quantum computation. SIAM Rev. 50, 755–787 (2008).
Boixo, S. et al. Evidence for quantum annealing with more than one hundred qubits. Nat. Phys. 10, 218–224 (2014).
Rønnow, T. F. et al. Defining and detecting quantum speedup. Science 345, 420–424 (2014).
Woo Shin, S., Smith, G., Smolin, J. A. & Vazirani, U. How ‘quantum’ is the D-Wave machine? Preprint at https://arxiv.org/abs/1401.7087 (2014).
Katzgraber, H. G., Hamze, F. & Andrist, R. S. Glassy chimeras could be blind to quantum speedup: designing better benchmarks for quantum annealing machines. Phys. Rev. X 4, 021008 (2015).
Venturelli, D. et al. Quantum optimization of fully connected spin glasses. Phys. Rev. X 5, 031040 (2015).
Hen, I. et al. Probing for quantum speedup in spin-glass problems with planted solutions. Phys. Rev. A 92, 042325 (2015).
Amin, M. H. Searching for quantum speedup in quasistatic quantum annealers. Phys. Rev. A 92, 052323 (2015).
Argüello-Luengo, J., González-Tudela, A., Shi, T., Zoller, P. & Cirac, J. I. Analogue quantum chemistry simulation. Nature 574, 215–218 (2019).
Bernien, H. et al. Probing many-body dynamics on a 51-atom quantum simulator. Nature 551, 579–584 (2017).
Zhang, J. et al. Observation of a many-body dynamical phase transition with a 53-qubit quantum simulator. Nature 551, 601–604 (2017).
Kokail, C. et al. Self-verifying variational quantum simulation of lattice models. Nature 569, 355–360 (2019).
Henriet, L. et al. Quantum computing with neutral atoms. Quantum 4, 327 (2020).
Pichler, H., Wang, S.-T., Zhou, L., Choi, S. & Lukin, M. D. Quantum optimization for maximum independent set using rydberg atom arrays. Preprint at https://arxiv.org/abs/1808.10816 (2018).
Serret, M. F., Marchand, B. & Ayral, T. Solving optimization problems with Rydberg analog quantum computers: realistic requirements for quantum advantage using noisy simulation and classical benchmarks. Preprint at https://arxiv.org/abs/2006.11190 (2020).
Bennett, C., Bernstein, E., Brassard, G. & Vazirani, U. Strengths and weaknesses of quantum computing. SIAM J. Comput. 26, 1510–1523 (1997).
Hollenberg, L. C. L. Fast quantum search algorithms in protein sequence comparisons: quantum bioinformatics. Phys. Rev. E 62, 7532 (2000).
Biamonte, J. et al. Quantum machine learning. Nature 549, 195–202 (2017).
Lloyd, S. UniversaI quantum simulators. Science 273, 1073–1078 (1997).
McArdle, S., Endo, S., Aspuru-Guzik, A., Benjamin, S. & Yuan, X. Quantum computational chemistry. Rev. Mod. Phys. 92, 015003 (2020).
Bauer, B., Bravyi, S., Motta, M. & Kin-Lic Chan, G. Quantum algorithms for quantum chemistry and quantum materials science. Chem. Rev. 120, 12685 (2020).
Cao, Y. et al. Quantum chemistry in the age of quantum computing. Chem. Rev. 119, 10856 (2019).
Cao, Y., Romero, J. & Aspuru-Guzik, A. Potential of quantum computing for drug discovery. IBM J. Res. Dev. 92, 1 (2018).
Harrow, A. W., Hassidim, A. & Lloyd, S. Quantum algorithm for linear systems of equations. Phys. Rev. Lett. 103, 150502 (2009).
Leyton, S. & Osborne, T. A quantum algorithm to solve nonlinear differential equations. Preprint at https://arxiv.org/abs/0812.4423 (2008).
Berry, D. High-order quantum algorithm for solving linear differential equations. J. Phys. A 47, 105301 (2014).
Childs, A. M. & Liu, J. P. Quantum spectral methods for differential equations. Commun. Math. Phys. 375, 1427–1457 (2020).
Childs, A. M., Liu, J. P. & Ostrander, A. High-precision quantum algorithms for partial differential equations. Preprint at https://arxiv.org/abs/2002.07868 (2020).
Alexandru, C.-M. et al. Quantum speedups of some general-purpose numerical optimization algorithms. Preprint at https://arxiv.org/abs/2004.06521 (2020).
Lucas, A. Ising formulations of many NP problems. Front. Phys. 2, 5 (2014).
Farhi, E. & Harrow, A. W. Quantum supremacy through the quantum approximate optimization algorithm. Preprint at https://arxiv.org/abs/1602.07674(2016).
Bishop, C. M. Pattern Recognition and Machine Learning (Springer, 2006).
Wild, D. S., Sels, D., Pichler, H., Zanoci, C. & Lukin, M. D. Quantum sampling algorithms for near-term devices. Preprint at https://arxiv.org/abs/2005.14059 (2020).
Löwdin, P.-O. Proton tunneling in DNA and its biological implications. Rev. Mod. Phys. 35, 724 (1963).
Cha, Y., Murray, C. J. & Klinman, J. P. Hydrogen tunneling in enzyme reactions. Science 4896, 1325–1330 (1989).
Huynh, M. H. V. & Meyer, T. J. Proton-coupled electron transfer. Chem. Rev. 107, 5004–5064 (2007).
Koronkiewicz, B., Swierk, J., Regan, K. & Mayer, J. M. Shallow distance dependence for proton-coupled tyrosine oxidation in oligoproline peptides. J. Am. Chem. Soc. 142, 12106–12118 (2020).
Carra, C., Iordanova, N. & Hammes-Schiffer, S. Proton-coupled electron transfer in a model for tyrosine oxidation in photosystem II. J. Am. Chem. Soc. 125, 10429–10436 (2003).
Hatcher, E., Soudackov, A. V. & Hammes-Schiffer, S. Proton-coupled electron transfer in soybean lipoxygenase. J. Am. Chem. Soc. 126, 5763–5775 (2004).
Einsle, O. & Rees, D. C. Structural enzymology of nitrogenase enzymes. Chem. Rev. 120, 4969–5004 (2020).
Reiher, M., Wiebe, N., Svore, K. M., Wecker, D. & Troyer, M. Elucidating reaction mechanisms on quantum computers. Proc. Natl Acad. Sci. USA 114, 7555–7560 (2017).
Berry, D. W., Gidney, C., Motta, M., McClean, J. R. & Babbush, R. Qubitization of arbitrary basis quantum chemistry leveraging sparsity and low rank factorization. Quantum 3, 208 (2019).
von Burg, V. et al. Quantum computing enhanced computational catalysis. Preprint at https://arxiv.org/abs/2007.14460 (2020).
Lee, J. et al. Even more efficient quantum computations of chemistry through tensor hypercontraction. Preprint at https://arxiv.org/abs/2011.03494 (2020).
Cheng, Y.-C. & Fleming, G. R. Dynamics of light harvesting in photosynthesis. Annu. Rev. Phys. Chem. 60, 241–262 (2009).
Polvka, T. & Sundström, V. Ultrafast dynamics of carotenoid excited states–from solution to natural and artificial systems. Chem. Rev. 104, 2021–2072 (2004).
Hahn, S. & Stock, G. Quantum-mechanical modeling of the femtosecond isomerization in rhodopsin. J. Phys. Chem. B 104, 1146–1149 (2000).
Andruniòw, T., Ferrè, N. & Olivucci, M. Structure, initial excited-state relaxation, and energy storage of rhodopsin resolved at the multiconfigurational perturbation theory level. Proc. Natl Acad. Sci. USA 101, 17908–17913 (2004).
Neugebauer, J. Photophysical properties of natural light-harvesting complexes studied by subsystem density functional theory. J. Phys. Chem. B 112, 2207–2217 (2008).
König, C. & Neugebauer, J. First-principles calculation of electronic spectra of light-harvesting complex II. Phys. Chem. Chem. Phys. 13, 10475–10490 (2011).
Dill, K. A. Theory for the folding and stability of globular proteins. Biochemistry 24, 1501–1509 (1985).
Dill, K. A. & MacCallum, J. L. The protein-folding problem, 50 years on. Science 338, 1042–1046 (2012).
Perdomo, A., Truncik, C., Tubert-Brohman, I., Rose, G. & Aspuru-Guzik, A. Construction of model Hamiltonians for adiabatic quantum computation and its application to finding low-energy conformations of lattice protein models. Phys. Rev. A 78, 012320 (2008).
Perdomo-Ortiz, A., Dickson, N., Drew-Brook, M., Rosem, G. & Aspuru-Guzik, A. Finding low-energy conformations of lattice protein models by quantum annealing. Sci. Rep. 2, 571 (2012).
Babbush, R., Perdomo-Ortiz, A., O’Gorman, B., Macready, W. & Aspuru-Guzik, A. in Advances in Chemical Physics Vol. 155 (eds. Rice, S. A. & Dinner, A. R.) Ch. 5 (2014).
Babej, T., Fingerhuth, M. & Ing, C. Coarse-grained Lattice Protein Folding on a Quantum Annealer Internal ProteinQure White Paper (ProteinQure,2018).
Fingerhuth, M. A quantum alternating operator ansatz with hard and soft constraints for lattice protein folding. Preprint at https://arxiv.org/abs/1810.13411 (2018).
Arute, F. et. al. Quantum approximate optimization of non-planar graph problems on a planar superconducting processor. Preprint at https://arxiv.org/abs/2004.04197 (2020).
Mulligan, V. K. et al. Designing peptides on a quantum computer. Preprint at bioRxiv https://doi.org/10.1101/752485 (2020).
Rohl, C. A., Strauss, C. E., Misura, K. M. & Baker, D. Protein structure prediction using Rosetta. Meth. Enzymol. 383, 66–93 (2004).
Marchand, D. J. J. et al. A variable neighbourhood descent heuristic for conformational search using a quantum annealer. Sci Rep. 9, 13708 (2019).
Li, R. Y., Di Felice, R., Rohs, R. & Lidar, D. A. Quantum annealing versus classical machine learning applied to a simplified computational biology problem. npj Quantum Inf. 4, 14 (2018).
Mittal, V. & McDonald, J. De novo assembly and characterization of breast cancer transcriptomes identifies large numbers of novel fusion-gene transcripts of potential functional significance. BMC Med. Genomics 10, 53 (2017).
Sarkar, A., Al-Ars, Z. & Bertels, K. QuASeR: quantum accelerated de novo DNA sequence reconstruction. Preprint at https://arxiv.org/abs/2004.05078 (2020).
Boev, A. S. et al. Genome assembly using quantum and quantum-inspired annealing. Preprint at https://arxiv.org/abs/2004.06719 (2020).
Tiunov, E. S., Ulanov, A. E. & Lvovsky, A. I. Annealing by simulating the coherent Ising machine. Opt. Express 27, 10288–10295 (2019).
Lindvall, O. B. Quantum Methods for Sequence Alignment and Metagenomics. PhD thesis (2019).
Sarkar, A., Al-Ars, Z., Almudever, C. G. & Bertels, K. An algorithm for DNA read alignment on quantum accelerators. Preprint at https://arxiv.org/abs/1909.05563 (2019).
Prousalis, K. & Konofaos, N. A quantum pattern recognition method for improving pairwise sequence alignment. Sci. Rep. 9, 7226 (2019).
Butenko, S. & Wilhelm, W. Clique-detection models in computational biochemistry and genomics. Eur. J. Oper. Res. 173, 1–17 (2006).
Callaway, E. ‘It will change everything’: DeepMind’s AI makes gigantic leap in solving protein structures. Nature 588, 203–204 (2020).
Acknowledgements
We thank A. Nizamieva, E. Kiktenko, A. Mastiukova and A. Favorov for useful comments and productive discussions. A.K.F. is supported by the Russian Science Foundation (19-71-10092). M.S.G. is supported by the Russian Foundation of Basic Research (18-29-13011). A.K.F. also acknowledges support from the Leading Research Center on Quantum Computing (agreement no. 014/20; analysis of quantum algorithms for NISQ devices).
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A.K.F. and M.S.G. equally contributed to this review. A.K.F. mostly participated in the part related to quantum computing, whereas M.S.G. focused on the biological applications.
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Owing to the employments and consulting activities of A.K.F., he has financial interests in the commercial applications of quantum computing.
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Peer review information Nature Computational Science thanks Koen Bertels, Göran Johansson and the other, anonymous, reviewer(s) for their contribution tof the peer review of this work. Fernando Chirigati was the primary editor on this Perspective and managed its editorial process and peer review in collaboration with the rest of the editorial team.
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Fedorov, A.K., Gelfand, M.S. Towards practical applications in quantum computational biology. Nat Comput Sci 1, 114–119 (2021). https://doi.org/10.1038/s43588-021-00024-z
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DOI: https://doi.org/10.1038/s43588-021-00024-z
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