Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Towards practical applications in quantum computational biology


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.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.


  1. 1.

    Moore, G. E. Cramming more components onto integrated circuits. IEEE Solid State Circuits Mag. 11, 33–35 (2006).

    Google Scholar 

  2. 2.

    Waldrop, M. M. The chips are down for Moore’s law. Nature 530, 144–147 (2016).

    Google Scholar 

  3. 3.

    Markov, I. L. Limits on fundamental limits to computation. Nature 512, 147–154 (2014).

    Google Scholar 

  4. 4.

    Manin, Y. I. Computable and Noncomputable (in Russian) (Sov. Radio, 1980).

  5. 5.

    Feynman, R. P. Simulating physics with computers. Int. J. Theor. Phys. 21, 467–488 (1982).

    MathSciNet  Google Scholar 

  6. 6.

    Feynman, R. P. Quantum mechanical computers. Found. Phys. 16, 507–531 (1986).

    MathSciNet  Google Scholar 

  7. 7.

    Ladd, T. D. et al. Quantum computers. Nature 464, 45–53 (2010).

    Google Scholar 

  8. 8.

    Preskill, J. Quantum computing in the NISQ era and beyond. Quantum 2, 79 (2018).

    Google Scholar 

  9. 9.

    Shor, P. W. Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer. SIAM J. Comput. 26, 1484–1509 (1997).

    MathSciNet  MATH  Google Scholar 

  10. 10.

    Aaronson, S. & Arkhipov, A. The computational complexity of linear optics. Theor. Comput. 9, 143–252 (2013).

    MathSciNet  MATH  Google Scholar 

  11. 11.

    Montanaro, A. Quantum algorithms: an overview. npj Quantum Inf. 2, 15023 (2016).

    Google Scholar 

  12. 12.

    Harrow, A. W. & Montanaro, A. Quantum computational supremacy. Nature 549, 203–209 (2017).

    Google Scholar 

  13. 13.

    Arute, F. et al. Quantum supremacy using a programmable superconducting processor. Nature 574, 505–510 (2019).

    Google Scholar 

  14. 14.

    Pednault, E., Gunnels, J. A., Nannicini, G., Horesh, L. & Wisnieff, R. Leveraging secondary storage to simulate deep 54-qubit sycamore circuits. Preprint at (2019).

  15. 15.

    Huang, C. et al. Classical simulation of quantum supremacy circuits. Preprint at (2020).

  16. 16.

    Zlokapa, A., Boixo, S. & Lidar, D. Boundaries of quantum supremacy via random circuit sampling. Preprint at (2020).

  17. 17.

    Zhong, Han-Sen et al. Quantum computational advantage using photons. Science 370, 1460–1463 (2020).

    Google Scholar 

  18. 18.

    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).

    Google Scholar 

  19. 19.

    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).

    Google Scholar 

  20. 20.

    Outeiral, C. et al. The prospects of quantum computing in computational molecular biology. WIREs Comput. Mol. Sci. 11, e1481 (2021).

    Google Scholar 

  21. 21.

    Lambert, N. et al. Quantum biology. Nat. Phys. 9, 10–18 (2013).

    Google Scholar 

  22. 22.

    Wang, B.-X. et al. Efficient quantum simulation of photosynthetic light harvesting. npj Quantum Inf. 4, 52 (2018).

    Google Scholar 

  23. 23.

    Emani, P. S. et al. Quantum computing at the frontiers of biological sciences. Nat. Methods (2021).

  24. 24.

    Preskill, J. in Introduction to Quantum Computation and Information 213–269 (World Scientific, 1998).

  25. 25.

    Albash, T. & Lidar, D. A. Adiabatic quantum computation. Rev. Mod. Phys. 90, 015002 (2018).

    MathSciNet  Google Scholar 

  26. 26.

    Aharonov, D. et al. Adiabatic quantum computation is equivalent to standard quantum computation. SIAM Rev. 50, 755–787 (2008).

    MathSciNet  MATH  Google Scholar 

  27. 27.

    Boixo, S. et al. Evidence for quantum annealing with more than one hundred qubits. Nat. Phys. 10, 218–224 (2014).

    Google Scholar 

  28. 28.

    Rønnow, T. F. et al. Defining and detecting quantum speedup. Science 345, 420–424 (2014).

    Google Scholar 

  29. 29.

    Woo Shin, S., Smith, G., Smolin, J. A. & Vazirani, U. How ‘quantum’ is the D-Wave machine? Preprint at (2014).

  30. 30.

    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).

  31. 31.

    Venturelli, D. et al. Quantum optimization of fully connected spin glasses. Phys. Rev. X 5, 031040 (2015).

    Google Scholar 

  32. 32.

    Hen, I. et al. Probing for quantum speedup in spin-glass problems with planted solutions. Phys. Rev. A 92, 042325 (2015).

    Google Scholar 

  33. 33.

    Amin, M. H. Searching for quantum speedup in quasistatic quantum annealers. Phys. Rev. A 92, 052323 (2015).

    Google Scholar 

  34. 34.

    Argüello-Luengo, J., González-Tudela, A., Shi, T., Zoller, P. & Cirac, J. I. Analogue quantum chemistry simulation. Nature 574, 215–218 (2019).

    Google Scholar 

  35. 35.

    Bernien, H. et al. Probing many-body dynamics on a 51-atom quantum simulator. Nature 551, 579–584 (2017).

    Google Scholar 

  36. 36.

    Zhang, J. et al. Observation of a many-body dynamical phase transition with a 53-qubit quantum simulator. Nature 551, 601–604 (2017).

    Google Scholar 

  37. 37.

    Kokail, C. et al. Self-verifying variational quantum simulation of lattice models. Nature 569, 355–360 (2019).

    Google Scholar 

  38. 38.

    Henriet, L. et al. Quantum computing with neutral atoms. Quantum 4, 327 (2020).

    Google Scholar 

  39. 39.

    Pichler, H., Wang, S.-T., Zhou, L., Choi, S. & Lukin, M. D. Quantum optimization for maximum independent set using rydberg atom arrays. Preprint at (2018).

  40. 40.

    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 (2020).

  41. 41.

    Bennett, C., Bernstein, E., Brassard, G. & Vazirani, U. Strengths and weaknesses of quantum computing. SIAM J. Comput. 26, 1510–1523 (1997).

    MathSciNet  MATH  Google Scholar 

  42. 42.

    Hollenberg, L. C. L. Fast quantum search algorithms in protein sequence comparisons: quantum bioinformatics. Phys. Rev. E 62, 7532 (2000).

    Google Scholar 

  43. 43.

    Biamonte, J. et al. Quantum machine learning. Nature 549, 195–202 (2017).

    Google Scholar 

  44. 44.

    Lloyd, S. UniversaI quantum simulators. Science 273, 1073–1078 (1997).

    Google Scholar 

  45. 45.

    McArdle, S., Endo, S., Aspuru-Guzik, A., Benjamin, S. & Yuan, X. Quantum computational chemistry. Rev. Mod. Phys. 92, 015003 (2020).

    MathSciNet  Google Scholar 

  46. 46.

    Bauer, B., Bravyi, S., Motta, M. & Kin-Lic Chan, G. Quantum algorithms for quantum chemistry and quantum materials science. Chem. Rev. 120, 12685 (2020).

    Google Scholar 

  47. 47.

    Cao, Y. et al. Quantum chemistry in the age of quantum computing. Chem. Rev. 119, 10856 (2019).

    Google Scholar 

  48. 48.

    Cao, Y., Romero, J. & Aspuru-Guzik, A. Potential of quantum computing for drug discovery. IBM J. Res. Dev. 92, 1 (2018).

    Google Scholar 

  49. 49.

    Harrow, A. W., Hassidim, A. & Lloyd, S. Quantum algorithm for linear systems of equations. Phys. Rev. Lett. 103, 150502 (2009).

    MathSciNet  Google Scholar 

  50. 50.

    Leyton, S. & Osborne, T. A quantum algorithm to solve nonlinear differential equations. Preprint at (2008).

  51. 51.

    Berry, D. High-order quantum algorithm for solving linear differential equations. J. Phys. A 47, 105301 (2014).

    MathSciNet  MATH  Google Scholar 

  52. 52.

    Childs, A. M. & Liu, J. P. Quantum spectral methods for differential equations. Commun. Math. Phys. 375, 1427–1457 (2020).

    MathSciNet  MATH  Google Scholar 

  53. 53.

    Childs, A. M., Liu, J. P. & Ostrander, A. High-precision quantum algorithms for partial differential equations. Preprint at (2020).

  54. 54.

    Alexandru, C.-M. et al. Quantum speedups of some general-purpose numerical optimization algorithms. Preprint at (2020).

  55. 55.

    Lucas, A. Ising formulations of many NP problems. Front. Phys. 2, 5 (2014).

    Google Scholar 

  56. 56.

    Farhi, E. & Harrow, A. W. Quantum supremacy through the quantum approximate optimization algorithm. Preprint at

  57. 57.

    Bishop, C. M. Pattern Recognition and Machine Learning (Springer, 2006).

  58. 58.

    Wild, D. S., Sels, D., Pichler, H., Zanoci, C. & Lukin, M. D. Quantum sampling algorithms for near-term devices. Preprint at (2020).

  59. 59.

    Löwdin, P.-O. Proton tunneling in DNA and its biological implications. Rev. Mod. Phys. 35, 724 (1963).

    Google Scholar 

  60. 60.

    Cha, Y., Murray, C. J. & Klinman, J. P. Hydrogen tunneling in enzyme reactions. Science 4896, 1325–1330 (1989).

    Google Scholar 

  61. 61.

    Huynh, M. H. V. & Meyer, T. J. Proton-coupled electron transfer. Chem. Rev. 107, 5004–5064 (2007).

    Google Scholar 

  62. 62.

    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).

    Google Scholar 

  63. 63.

    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).

    Google Scholar 

  64. 64.

    Hatcher, E., Soudackov, A. V. & Hammes-Schiffer, S. Proton-coupled electron transfer in soybean lipoxygenase. J. Am. Chem. Soc. 126, 5763–5775 (2004).

    Google Scholar 

  65. 65.

    Einsle, O. & Rees, D. C. Structural enzymology of nitrogenase enzymes. Chem. Rev. 120, 4969–5004 (2020).

    Google Scholar 

  66. 66.

    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).

    Google Scholar 

  67. 67.

    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).

    Google Scholar 

  68. 68.

    von Burg, V. et al. Quantum computing enhanced computational catalysis. Preprint at (2020).

  69. 69.

    Lee, J. et al. Even more efficient quantum computations of chemistry through tensor hypercontraction. Preprint at (2020).

  70. 70.

    Cheng, Y.-C. & Fleming, G. R. Dynamics of light harvesting in photosynthesis. Annu. Rev. Phys. Chem. 60, 241–262 (2009).

    Google Scholar 

  71. 71.

    Polvka, T. & Sundström, V. Ultrafast dynamics of carotenoid excited states–from solution to natural and artificial systems. Chem. Rev. 104, 2021–2072 (2004).

    Google Scholar 

  72. 72.

    Hahn, S. & Stock, G. Quantum-mechanical modeling of the femtosecond isomerization in rhodopsin. J. Phys. Chem. B 104, 1146–1149 (2000).

    Google Scholar 

  73. 73.

    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).

    Google Scholar 

  74. 74.

    Neugebauer, J. Photophysical properties of natural light-harvesting complexes studied by subsystem density functional theory. J. Phys. Chem. B 112, 2207–2217 (2008).

    Google Scholar 

  75. 75.

    König, C. & Neugebauer, J. First-principles calculation of electronic spectra of light-harvesting complex II. Phys. Chem. Chem. Phys. 13, 10475–10490 (2011).

    Google Scholar 

  76. 76.

    Dill, K. A. Theory for the folding and stability of globular proteins. Biochemistry 24, 1501–1509 (1985).

    Google Scholar 

  77. 77.

    Dill, K. A. & MacCallum, J. L. The protein-folding problem, 50 years on. Science 338, 1042–1046 (2012).

    Google Scholar 

  78. 78.

    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).

    Google Scholar 

  79. 79.

    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).

    Google Scholar 

  80. 80.

    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).

  81. 81.

    Babej, T., Fingerhuth, M. & Ing, C. Coarse-grained Lattice Protein Folding on a Quantum Annealer Internal ProteinQure White Paper (ProteinQure,2018).

  82. 82.

    Fingerhuth, M. A quantum alternating operator ansatz with hard and soft constraints for lattice protein folding. Preprint at (2018).

  83. 83.

    Arute, F. et. al. Quantum approximate optimization of non-planar graph problems on a planar superconducting processor. Preprint at (2020).

  84. 84.

    Mulligan, V. K. et al. Designing peptides on a quantum computer. Preprint at bioRxiv (2020).

  85. 85.

    Rohl, C. A., Strauss, C. E., Misura, K. M. & Baker, D. Protein structure prediction using Rosetta. Meth. Enzymol. 383, 66–93 (2004).

    Google Scholar 

  86. 86.

    Marchand, D. J. J. et al. A variable neighbourhood descent heuristic for conformational search using a quantum annealer. Sci Rep. 9, 13708 (2019).

    Google Scholar 

  87. 87.

    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).

    Google Scholar 

  88. 88.

    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).

    Google Scholar 

  89. 89.

    Sarkar, A., Al-Ars, Z. & Bertels, K. QuASeR: quantum accelerated de novo DNA sequence reconstruction. Preprint at (2020).

  90. 90.

    Boev, A. S. et al. Genome assembly using quantum and quantum-inspired annealing. Preprint at (2020).

  91. 91.

    Tiunov, E. S., Ulanov, A. E. & Lvovsky, A. I. Annealing by simulating the coherent Ising machine. Opt. Express 27, 10288–10295 (2019).

    Google Scholar 

  92. 92.

    Lindvall, O. B. Quantum Methods for Sequence Alignment and Metagenomics. PhD thesis (2019).

  93. 93.

    Sarkar, A., Al-Ars, Z., Almudever, C. G. & Bertels, K. An algorithm for DNA read alignment on quantum accelerators. Preprint at (2019).

  94. 94.

    Prousalis, K. & Konofaos, N. A quantum pattern recognition method for improving pairwise sequence alignment. Sci. Rep. 9, 7226 (2019).

    Google Scholar 

  95. 95.

    Butenko, S. & Wilhelm, W. Clique-detection models in computational biochemistry and genomics. Eur. J. Oper. Res. 173, 1–17 (2006).

    MathSciNet  MATH  Google Scholar 

  96. 96.

    Callaway, E. ‘It will change everything’: DeepMind’s AI makes gigantic leap in solving protein structures. Nature 588, 203–204 (2020).

    Google Scholar 

Download references


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).

Author information




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.

Corresponding author

Correspondence to A. K. Fedorov.

Ethics declarations

Competing interests

Owing to the employments and consulting activities of A.K.F., he has financial interests in the commercial applications of quantum computing.

Additional information

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.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Fedorov, A.K., Gelfand, M.S. Towards practical applications in quantum computational biology. Nat Comput Sci 1, 114–119 (2021).

Download citation


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing