Van Noorden, R, Maher, B, Nuzzo, R. The top 100 papers. Nat News. 2014;514(7524):550.

Lee, C, Yang, W, Parr, RG. Development of the Colle–Salvetti correlation‐energy formula into a functional of the electron density. Phys Rev B. 1988;37(2):785.

Becke, AD. Density‐functional exchange‐energy approximation with correct asymptotic behavior. Phys Rev A. 1988;38(6):3098.

Perdew, JP, Burke, K, Ernzerhof, M. Generalized gradient approximation made simple. Phys Rev Lett. 1996;77(18):3865.

Becke, AD. Density‐functional thermochemistry. III. The role of exact exchange. J Chem Phys. 1993;98(7):5648–5652.

Thompson, JD, Higgins, DG, Gibson, TJ. CLUSTAL W: Improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position‐specific gap penalties and weight matrix choice. Nucleic Acids Res. 1994;22(22):4673–4680.

Altschul, SF, Gish, W, Miller, W, Myers, EW, Lipman, DJ. Basic local alignment search tool. J Mol Biol. 1990;215(3):403–410.

Altschul, SF, Madden, TL, Schäffer, AA, et al. Gapped BLAST and PSI‐BLAST: A new generation of protein database search programs. Nucleic Acids Res. 1997;25(17):3389–3402.

Saitou, N, Nei, M. The neighbor‐joining method: A new method for reconstructing phylogenetic trees. Mol Biol Evol. 1987;4(4):406–425.

Sheldrick, GM. A short history of SHELX. Acta Crystallogr Sect A: Found Crystallogr. 2008;64(1):112–122.

Otwinowski, Z, Minor, W. [20] processing of X‐ray diffraction data collected in oscillation mode. Methods in enzymology. Volume 276. Amsterdam: Elsevier, 1997; p. 307–326.

Feynman, RP. Simulating physics with computers. Int J Theor Phys. 1982;21(6):467–488.

Manin, Y. Вычислимое и невычислимое (Computable and uncomputable). Vol 128. Moscow: Sovetskoye Radio, 1980.

Szabo, A, Ostlund, NS. Modern quantum chemistry: Introduction to advanced electronic structure theory. Mineola, NY: Dover, 2012.

Kitaev, AY. Quantum measurements and the abelian stabilizer problem. *arXiv preprint quant‐ph/9511026*; 1995.

Harrow, AW, Hassidim, A, Lloyd, S. Quantum algorithm for linear systems of equations. Phys Rev Lett. 2009;103(15):150502.

Albash, T, Lidar, DA. Adiabatic quantum computation. Rev Mod Phys. 2018;90(1):015002.

Cao, Y, Romero, J, Aspuru‐Guzik, A. Potential of quantum computing for drug discovery. IBM J Res Dev. 2018;62(6):6–1.

Bi‐Xue, W, Ming‐Jie, T, Ai, Q, et al. Efficient quantum simulation of photosynthetic light harvesting. NPJ Quant Inform. 2018;4:1–6.

Arute, F, Arya, K, Babbush, R, et al. Quantum supremacy using a programmable superconducting processor. Nature. 2019;574(7779):505–510.

Pednault, E, Gunnels, JA, Nannicini, G, Horesh, L, Wisnieff, R. Leveraging secondary storage to simulate deep 54‐qubit Sycamore circuits. *arXiv preprint arXiv:1910.09534*; 2019.

Preskill, J. Quantum computing in the NISQ era and beyond. Quantum. 2018;2:79.

Biamonte, J, Wittek, P, Pancotti, N, Rebentrost, P, Wiebe, N, Lloyd, S. Quantum machine learning. Nature. 2017;549(7671):195–202.

Ciliberto, C, Herbster, M, Ialongo, AD, et al. Quantum machine learning: A classical perspective. Proc R Soc A: Math Phys Eng Sci. 2018;474(2209):20170551.

Dunjko, V, Briegel, HJ. Machine learning %26 artificial intelligence in the quantum domain: A review of recent progress. Rep Prog Phys. 2018;81(7):074001.

Cao, Y, Romero, J, Olson, JP, et al. Quantum chemistry in the age of quantum computing. Chem Rev. 2019;119(19):10856–10915.

McArdle, S, Endo, S, Aspuru‐Guzik, A, Benjamin, S, Yuan, X. Quantum computational chemistry. Rev Mod Phys. 2020;92(1):015003.

Bauer, B, Bravyi, S, Motta, M, Chan, GK. Quantum algorithms for quantum chemistry and quantum materials science, *arXiv preprint arXiv:2001.03685*, 2020.

Emani, PS, Warrell, J, Anticevic, A, et al. Quantum computing at the frontiers of biological sciences. *arXiv preprint arXiv:1911.07127*; 2019.

Montanaro, A. Quantum pattern matching fast on average. Algorithmica. 2017;77(1):16–39.

Kiani, BT, Villanyi, A, Lloyd, S. Quantum medical imaging algorithms. *arXiv preprint arXiv:2004.02036*; 2020

Childs, AM, Liu, JP. Quantum spectral methods for differential equations. Commun Math Phys. 2020;375:1–31.

Childs, AM, Liu, JP, Ostrander, A. High‐precision quantum algorithms for partial differential equations. *arXiv preprint arXiv:2002.07868*; 2020.

Alexandru, CM, Bridgett‐Tomkinson, E, Linden, N, MacManus, J, Montanaro, A, Morris, H. Quantum speedups of some general‐purpose numerical optimisation algorithms. *arXiv preprint arXiv:2004.06521*, 2020.

Ushijima‐Mwesigwa, H, Negre, CF, Mniszewski, SM. Graph partitioning using quantum annealing on the D‐Wave system. Proceedings of the Second International Workshop on Post Moores Era Supercomputing. New York: ACM, 2017; p. 22–29.

Negre, CF, Ushijima‐Mwesigwa, H, Mniszewski, SM. Detecting multiple communities using quantum annealing on the D‐Wave system. *arXiv preprint arXiv:1901.09756*; 2019.

Paparo, GD, Martin‐Delgado, M. Google in a quantum network. Sci Rep. 2012;2:444.

Izaac, JA, Zhan, X, Bian, Z, et al. Centrality measure based on continuous‐time quantum walks and experimental realization. Phys Rev A. 2017;95(3):032318.

Nielsen, MA, Chuang, I. Quantum computation and quantum information. Cambridge: Cambridge University Press, 2002.

Steane, A. The ion trap quantum information processor. Appl Phys B: Lasers Opt. 1997;64(6):623–643.

O`brien, JL. Optical quantum computing. Science. 2007;318(5856):1567–1570.

Schrödinger, E. Die gegenwärtige situation in der quantenmechanik. Naturwissenschaften. 1935;23(50):844–849.

Einstein, A, Podolsky, B, Rosen, N. Can quantum‐mechanical description of physical reality be considered complete? Phys Rev. 1935;47(10):777.

Gottesman, D. The Heisenberg representation of quantum computers, *arXiv preprint quant‐ph/9807006*; 1998.

Jozsa, R, Linden, N. On the role of entanglement in quantum‐computational speed‐up. Proc R Soc Lond Ser A: Math Phys Eng Sci. 2003;459(2036):2011–2032.

Harty, T, Allcock, D, Ballance, CJ, et al. High‐fidelity preparation, gates, memory, and readout of a trapped‐ion quantum bit. Phys Rev Lett. 2014;113(22):220501.

Barenco, A, Bennett, CH, Cleve, R, et al. Elementary gates for quantum computation. Phys Rev A. 1995;52(5):3457.

Devoret, MH, Schoelkopf, RJ. Superconducting circuits for quantum information: An outlook. Science. 2013;339(6124):1169–1174.

Ballance, C, Harty, T, Linke, N, Sepiol, M, Lucas, D. High‐fidelity quantum logic gates using trapped‐ion hyperfine qubits. Phys Rev Lett. 2016;117(6):060504.

Gottesman, D. Stabilizer codes and quantum error correction. *arXiv preprint quant‐ph/9705052*; 1997.

Kitaev, AY. Quantum computations: Algorithms and error correction. Uspekhi Mate Nauk. 1997;52(6):53–112.

Preskill, J. Fault‐tolerant quantum computation. Introduction to quantum computation and information. Singapore: World Scientific, 1998; p. 213–269.

Aharonov, D, Ben‐Or, M. Fault‐tolerant quantum computation with constant error rate. *arXiv preprint quant‐ph/9906129*; 1999.

Fowler, AG, Mariantoni, M, Martinis, JM, Cleland, AN. Surface codes: Towards practical large‐scale quantum computation. Phys Rev A. 2012;86(3):032324.

Shor, PW. Algorithms for quantum computation: Discrete logarithms and factoring. Proceedings 35th Annual Symposium on Foundations of Computer Science. New York: IEEE, 1994; p. 124–134.

McArdle, S, Yuan, X, Benjamin, S. Error‐mitigated digital quantum simulation. Phys Rev Lett. 2019;122(18):180501.

Li, Y, Benjamin, SC. Efficient variational quantum simulator incorporating active error minimization. Phys Rev X. 2017;7(2):021050.

Temme, K, Bravyi, S, Gambetta, JM. Error mitigation for short‐depth quantum circuits. Phys Rev Lett. 2017;119(18):180509.

Durbin, R, Eddy, SR, Krogh, A, Mitchison, G. Biological sequence analysis: Probabilistic models of proteins and nucleic acids. Cambridge: Cambridge University Press, 1998.

Zhang, L, Tan, J, Han, D, Zhu, H. From machine learning to deep learning: Progress in machine intelligence for rational drug discovery. Drug Discov Today. 2017;22(11):1680–1685.

Wang, S, Sun, S, Li, Z, Zhang, R, Xu, J. Accurate de novo prediction of protein contact map by ultra‐deep learning model. PLoS Comput Biol. 2017;13(1):e1005324.

Wang, S, Peng, J, Ma, J, Xu, J. Protein secondary structure prediction using deep convolutional neural fields. Sci Rep. 2016;6:18962.

Evans, R, Jumper, J, Kirkpatrick, J, et al., De novo structure prediction with deep‐learning based scoring.

Arunachalam, S, de Wolf, R. Guest column: A survey of quantum learning theory. ACM SIGACT News. 2017;48(2):41–67.

Schuld, M, Sinayskiy, I, Petruccione, F. Prediction by linear regression on a quantum computer. Phys Rev A. 2016;94(2):022342.

Wang, G. Quantum algorithm for linear regression. Phys Rev A. 2017;96(1):012335.

Dutta, S, Suau, A, Dutta, S, Roy, S, Behera, BK, Panigrahi, BK. Demonstration of a quantum circuit design methodology for multiple regression. *arXiv preprint arXiv:1811.01726*; 2018.

Li, G, Wang, Y, Luo, Y, Feng, Y. Quantum data fitting algorithm for non‐sparse matrices. *arXiv preprint arXiv:1907.06949*; 2019.

Zhao, Z, Fitzsimons, JK, Fitzsimons, JF. Quantum‐assisted Gaussian process regression. Phys Rev A. 2019;99(5):052331.

Zhao, Z, Fitzsimons, JK, Osborne, MA, Roberts, SJ, Fitzsimons, JF. Quantum algorithms for training Gaussian processes. Phys Rev A. 2019;100(1):012304.

Lu, S, Braunstein, SL. Quantum decision tree classifier. Quant Inform Process. 2014;13(3):757–770.

Schuld, M, Petruccione, F. Quantum ensembles of quantum classifiers. Sci Rep. 2018;8(1):2772.

Wang, X, Ma, Y, Hsieh, M‐H, Yung, M. Quantum speedup in adaptive boosting of binary classification, *arXiv preprint arXiv:1902.00869*; 2019.

Arunachalam, S, Maity, R. Quantum boosting. *arXiv preprint arXiv:2002.05056*; 2020.

Rebentrost, P, Mohseni, M, Lloyd, S. Quantum support vector machine for big data classification. Phys Rev Lett. 2014;113(13):130503.

Chatterjee, R, Yu, T. Generalized coherent states, reproducing kernels, and quantum support vector machines. *arXiv preprint arXiv:1612.03713*; 2016.

Schuld, M, Killoran, N. Quantum machine learning in feature Hilbert spaces. Phys Rev Lett. 2019;122(4):040504.

Monras, A, Beige, A, Wiesner, K. Hidden quantum Markov models and non‐adaptive read‐out of many‐body states. *arXiv preprint arXiv:1002.2337*; 2010.

Srinivasan, S, Gordon, G, Boots, B. Learning hidden quantum Markov models. *arXiv preprint arXiv:1710.09016*; 2017.

Low, GH, Yoder, TJ, Chuang, IL. Quantum inference on Bayesian networks. Phys Rev A. 2014;89(6):062315.

Wiebe, B, Granade, C. Can small quantum systems learn?, *arXiv preprint arXiv:1512.03145*; 2015.

Benedetti, M, Realpe‐Gómez, J, Biswas, R, Perdomo‐Ortiz, A. Quantum‐assisted learning of hardware‐embedded probabilistic graphical models. Phys Rev X. 2017;7(4):041052.

Lloyd, S, Mohseni, M, Rebentrost, P. Quantum algorithms for supervised and unsupervised machine learning. *arXiv preprint arXiv:1307.0411*; 2013.

Wiebe, N, Kapoor, A, Svore, KM. Quantum nearest‐neighbor algorithms for machine learning. Quant Inform Comput. 2018;15:318–358.

Kerenidis, I, Landman, J, Luongo, A, Prakash, A. Q‐means: A quantum algorithm for unsupervised machine learning. Advances in Neural Information Processing Systems. New York: Curran Associates, 2019; p. 4136–4146.

Lloyd, S, Mohseni, M, Rebentrost, P. Quantum principal component analysis. Nat Phys. 2014;10(9):631.

Lloyd, S, Garnerone, S, Zanardi, P. Quantum algorithms for topological and geometric analysis of data. Nat Commun. 2016;7:10138.

Kerenidis, I, Luongo, A, Prakash, A. Quantum expectation‐maximization for Gaussian mixture models. *arXiv preprint arXiv:1908.06657*. 2019.

Miyahara, H, Aihara, K, Lechner, W. Quantum expectation–maximization algorithm. *arXiv preprint arXiv:1908.06655*; 2019.

Khoshaman, A, Vinci, W, Denis, B, Andriyash, E, Amin, MH. Quantum variational autoencoder. Quant Sci Technol. 2018;4(1):014001.

Kak, SC. Quantum neural computing. Advances in imaging and electron physics. Volume 94. Amsterdam: Elsevier, 1995; p. 259–313.

Zak, M, Williams, CP. Quantum neural nets. Int J Theor Phys. 1998;37(2):651–684.

Cao, Y, Guerreschi, GG, Aspuru‐Guzik, A. Quantum neuron: An elementary building block for machine learning on quantum computers. *arXiv preprint arXiv:1711.11240*; 2017.

Wan, KH, Dahlsten, O, Kristjánsson, H, Gardner, R, Kim, M. Quantum generalisation of feedforward neural networks. NPJ Quant Inform. 2017;3(1):36.

Killoran, N, Bromley, TR, Arrazola, JM, Schuld, M, Quesada, N, Lloyd, S. Continuous‐variable quantum neural networks. Phys Rev Res. 2019;1(3):033063.

Cong, I, Choi, S, Lukin, MD. Quantum convolutional neural networks. Nat Phys. 2019;15(12):1273–1278.

Zhao, Z, Pozas‐Kerstjens, A, Rebentrost, P, Wittek, P. Bayesian deep learning on a quantum computer. Quant Mach Intell. 2019;1(1–2):41–51.

Lloyd, S, Weedbrook, C. Quantum generative adversarial learning. Phys Rev Lett. 2018;121(4):040502.

Dallaire‐Demers, P‐L, Killoran, N. Quantum generative adversarial networks. Phys Rev A. 2018;98(1):012324.

Gao, X, Zhang, Z, Duan, L. An efficient quantum algorithm for generative machine learning. *arXiv preprint arXiv:1711.02038*; 2017.

M. Denil, and N. De Freitas,. NIPS 2011 Deep Learning and Unsupervised Feature Learning Workshop. Toward the implementation of a quantum RBM. New York: Curran Associates, 2011.

Dumoulin, V, Goodfellow, IJ, Courville, A, Bengio, Y. On the challenges of physical implementations of rbms. Twenty‐eighth AAAI conference on artificial intelligence. Palo Alto, California: Association for the Advancement of Artificial Intelligence, 2014.

Benedetti, M, Realpe‐Gómez, J, Biswas, R, Perdomo‐Ortiz, A. Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning. Phys Rev A. 2016;94(2):022308.

Wiebe, N, Kapoor, A, Svore, KM. Quantum deep learning. *arXiv preprint arXiv:1412.3489*; 2014.

Anschuetz, ER, Cao, Y. Realizing quantum Boltzmann machines through eigenstate thermalization. *arXiv preprint arXiv:1903.01359*; 2019.

Dunjko, V, Taylor, JM, Briegel, HJ. Quantum‐enhanced machine learning. Phys Rev Lett. 2016;117(13):130501.

Dunjko, V, Taylor, JM, Briegel, HJ. Advances in quantum reinforcement learning. 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC). New York: IEEE, 2017; p. 282–287.

LeCun, Y, Bengio, Y, Hinton, G. Deep learning. Nature. 2015;521(7553):436–444.

Aaronson, S. Read the fine print. Nat Phys. 2015;11(4):291.

Giovannetti, V, Lloyd, S, Maccone, L. Quantum random access memory. Phys Rev Lett. 2008;100(16):160501.

Giovannetti, V, Lloyd, S, Maccone, L. Architectures for a quantum random access memory. Phys Rev A. 2008;78(5):052310.

Hong, F‐Y, Xiang, Y, Zhu, Z‐Y, Jiang, L‐z, Wu, L‐n. Robust quantum random access memory. Phys Rev A. 2012;86(1):010306.

Park, DK, Petruccione, F, Rhee, J‐KK. Circuit‐based quantum random access memory for classical data. Sci Rep. 2019;9(1):3949.

Stokes, JM, Yang, K, Swanson, K, et al. A deep learning approach to antibiotic discovery. Cell. 2020;180(4):688–702.

Häse, F, Roïc, LM, Aspuru‐Guzik, A. Next‐generation experimentation with self‐driving laboratories. Trends Chem. 2019;1(3):282–291.

Holm, L, Rosenström, P. Dali server: Conservation mapping in 3D. Nucleic Acids Res. 2010;38:W545–W549.

Libbrecht, MW, Noble, WS. Machine learning applications in genetics and genomics. Nat Rev Genet. 2015;16(6):321–332.

Bishop, CM. Pattern recognition and machine learning. Berlin: Springer Science + Business Media, 2006.

Ringnér, M. What is principal component analysis? Nat Biotechnol. 2008;26(3):303.

Topaz, CM, Ziegelmeier, L, Halverson, T. Topological data analysis of biological aggregation models. PLoS One. 2015;10(5):e0126383.

Horak, D, Maletić, S, Rajković, M. Persistent homology of complex networks. J Stat Mech: Theory Exp. 2009;2009(3):P03034.

Wójcikowski, M, Ballester, PJ, Siedlecki, P. Performance of machine‐learning scoring functions in structure‐based virtual screening. Sci Rep. 2017;7:46710.

Fatima, M, Pasha, M. Survey of machine learning algorithms for disease diagnostic. J Intell Learn Syst Appl. 2017;9(1):1.

Burbidge, R, Trotter, M, Buxton, B, Holden, S. Drug design by machine learning: Support vector machines for pharmaceutical data analysis. Comput Chem. 2001;26(1):5–14.

Shahriari, B, Swersky, K, Wang, Z, Adams, RP, De Freitas, N. Taking the human out of the loop: A review of Bayesian optimization. Proc IEEE. 2015;104(1):148–175.

Obrezanova, O, Csányi, G, Gola, JM, Segall, MD. Gaussian processes: A method for automatic QSAR modeling of ADME properties. J Chem Inf Model. 2007;47(5):1847–1857.

Kolb, B, Marshall, P, Zhao, B, Jiang, B, Guo, H. Representing global reactive potential energy surfaces using Gaussian processes. Chem A Eur J. 2017;121(13):2552–2557.

Ching, T, Himmelstein, DS, Beaulieu‐Jones, BK, et al. Opportunities and obstacles for deep learning in biology and medicine. J R Soc Interface. 2018;15(141):20170387.

Gómez‐Bombarelli, R, Wei, JN, Duvenaud, D, et al. Automatic chemical design using a data‐driven continuous representation of molecules. ACS Central Sci. 2018;4(2):268–276.

Smith, JS, Isayev, O, Roitberg, AE. ANI‐1: An extensible neural network potential with dft accuracy at force field computational cost. Chem Sci. 2017;8(4):3192–3203.

Yao, K, Herr, JE, Toth, DW, Mckintyre, R, Parkhill, J. The TensorMol‐0.1 model chemistry: A neural network augmented with long‐range physics. Chem Sci. 2018;9(8):2261–2269.

Sanchez‐Lengeling, B, Outeiral, C, Guimaraes, GL, Aspuru‐Guzik, A. Optimizing distributions over molecular space. An Objective‐Reinforced Generative Adversarial Network for Inverse‐design Chemistry (ORGANIC). ChemRxiv. 2017. https://doi.org/10.26434/chemrxiv.5309668.v3.

Sanchez‐Lengeling, B, Aspuru‐Guzik, A. Inverse molecular design using machine learning: Generative models for matter engineering. Science. 2018;361(6400):360–365.

Amin, MH, Andriyash, E, Rolfe, J, Kulchytskyy, B, Melko, R. Quantum Boltzmann machine. Phys Rev X. 2018;8(2):021050.

Kieferová, M, Wiebe, N. Tomography and generative training with quantum Boltzmann machines. Phys Rev A. 2017;96(6):062327.

Booth, GH, Thom, AJ, Alavi, A. Fermion Monte Carlo without fixed nodes: A game of life, death, and annihilation in slater determinant space. J Chem Phys. 2009;131(5):054106.

Hohenberg, P, Kohn, W. Inhomogeneous electron gas. Phys Rev. 1964;136(3B):B864.

Kohn, W, Sham, LJ. Self‐consistent equations including exchange and correlation effects. Phys Rev. 1965;140(4A):A1133.

Cohen, AJ, Mori‐Sánchez, P, Yang, W. Insights into current limitations of density functional theory. Science. 2008;321(5890):792–794.

Lloyd, S. Universal quantum simulators. Science. 1996;273:1073–1078.

Aspuru‐Guzik, A, Dutoi, AD, Love, PJ, Head‐Gordon, M. Simulated quantum computation of molecular energies. Science. 2005;309(5741):1704–1707.

Aspuru‐Guzik, A, Lindh, R, Reiher, M. The matter simulation (r)evolution. ACS Central Sci. 2018;4(2):144–152.

Abrams, DS, Lloyd, S. Simulation of many‐body Fermi systems on a universal quantum computer. Phys Rev Lett. 1997;79(13):2586.

Löwdin, P‐O. Proton tunneling in DNA and its biological implications. Rev Mod Phys. 1963;35(3):724.

Cha, Y, Murray, CJ, Klinman, JP. Hydrogen tunneling in enzyme reactions. Science. 1989;243(4896):1325–1330.

Veis, L, Višňák, J, Fleig, T, et al. Relativistic quantum chemistry on quantum computers. Phys Rev A. 2012;85(3):030304.

Lippard, SJ, Berg, JM. Principles of bioinorganic chemistry. Vol 70. Mill Valley, CA: University Science Books, 1994.

Reiher, M, Wiebe, N, Svore, KM, Wecker, D, Troyer, M. Elucidating reaction mechanisms on quantum computers. Proc Natl Acad Sci U S A. 2017;114(29):7555–7560.

Berry, DW, Gidney, C, Motta, M, McClean, JR, Babbush, R. Qubitization of arbitrary basis quantum chemistry leveraging sparsity and low rank factorization. Quantum. 2019;3:208.

Elfving, VE, Gámez, JA, Gogolin, C. Simulating quantum chemistry in the restricted Hartree–Fock space on a qubit‐based quantum computing device. *arXiv preprint arXiv:2002.00035*; 2020

Google, AI, Quantum and Collaborators. Hartree–Fock on a superconducting qubit quantum computer. *arXiv preprint arXiv:2004.04174*; 2020.

van der Kamp, MW, Mulholland, AJ. Combined quantum mechanics/molecular mechanics (QM/MM) methods in computational enzymology. Biochemistry. 2013;52(16):2708–2728.

Kandala, A, Mezzacapo, A, Temme, K, et al. Hardware‐efficient variational quantum eigensolver for small molecules and quantum magnets. Nature. 2017;549(7671):242.

O`Malley, PJ, Babbush, R, Kivlichan, ID, et al. Scalable quantum simulation of molecular energies. Phys Rev X. 2016;6(3):031007.

Shen, Y, Zhang, X, Zhang, S, Zhang, J‐N, Yung, M‐H, Kim, K. Quantum implementation of the unitary coupled cluster for simulating molecular electronic structure. Phys Rev A. 2017;95(2):020501.

Colless, JI, Ramasesh, VV, Dahlen, D, et al. Computation of molecular spectra on a quantum processor with an error‐resilient algorithm. Phys Rev X. 2018;8(1):011021.

Tubman, NM, Mejuto‐Zaera, C, Epstein, JM, et al. Postponing the orthogonality catastrophe: Efficient state preparation for electronic structure simulations on quantum devices. *arXiv preprint arXiv:1809.05523*; 2018.

Helgaker, T, Jorgensen, P, Olsen, J. Molecular electronic‐structure theory. New York: John Wiley %26 Sons, 2014.

Bravyi, SB, Kitaev, AY. Fermionic quantum computation. Ann Phys Rehabil Med. 2002;298(1):210–226.

Seeley, JT, Richard, MJ, Love, PJ. The Bravyi–Kitaev transformation for quantum computation of electronic structure. J Chem Phys. 2012;137(22):224109.

Berry, DW, Kieferová, M, Scherer, A, et al. Improved techniques for preparing eigenstates of fermionic hamiltonians. npj Quant Inform. 2018;4(1):22.

Kassal, I, Jordan, SP, Love, PJ, Mohseni, M, Aspuru‐Guzik, A. Polynomial‐time quantum algorithm for the simulation of chemical dynamics. Proc Natl Acad Sci U S A. 2008;105(48):18681–18686.

Lidar, DA, Wang, H. Calculating the thermal rate constant with exponential speedup on a quantum computer. Phys Rev E. 1999;59(2):2429.

Trotter, HF. On the product of semi‐groups of operators. Proc Am Math Soc. 1959;10(4):545–551.

Whitfield, JD, Biamonte, J, Aspuru‐Guzik, A. Simulation of electronic structure hamiltonians using quantum computers. Mol Phys. 2011;109(5):735–750.

Low, GH, Chuang, IL. Hamiltonian simulation by qubitization. Quantum. 2019;3:163.

Low, GH, Chuang, IL. Optimal hamiltonian simulation by quantum signal processing. Phys Rev Lett. 2017;118(1):010501.

Biamonte, J. Universal variational quantum computation. *arXiv preprint arXiv:1903.04500*; 2019.

Peruzzo, A, McClean, J, Shadbolt, P, et al. A variational eigenvalue solver on a photonic quantum processor. Nat Commun. 2014;5:4213.

McClean, JR, Romero, J, Babbush, R, Aspuru‐Guzik, A. The theory of variational hybrid quantum‐classical algorithms. New J Phys. 2016;18(2):023023.

McClean, JR, Babbush, R, Love, PJ, Aspuru‐Guzik, A. Exploiting locality in quantum computation for quantum chemistry. J Phys Chem Lett. 2014;5(24):4368–4380.

Messiah, A. Quantum mechanics. Mineola, NY: Dover, 2000.

Bartlett, RJ, Kucharski, SA, Noga, J. Alternative coupled‐cluster ansätze II. The unitary coupled‐cluster method. Chem Phys Lett. 1989;155(1):133–140.

McClean, JR, Boixo, S, Smelyanskiy, VN, Babbush, R, Neven, H. Barren plateaus in quantum neural network training landscapes. Nat Commun. 2018;9(1):4812.

McArdle, S, Jones, T, Endo, S, Li, Y, Benjamin, SC, Yuan, X. Variational ansatz‐based quantum simulation of imaginary time evolution. npj Quant Inform. 2019;5(1):1–6.

Chowdhury, AN, Low, GH, Wiebe, N. A variational quantum algorithm for preparing quantum Gibbs states. *arXiv preprint arXiv:2002.00055*; 2020

Dill, KA, MacCallum, JL. The protein‐folding problem, 50 years on. Science. 2012;338(6110):1042–1046.

Newman, M. Networks. Oxford: Oxford University Press, 2018.

D‐Wave Systems Inc.D‐Wave problem‐solving handbook. Vancouver, British Columbia: D‐Wave Systems Inc., 2019.

D‐Wave Systems Inc.Technical description of the D‐Wave quantum processing unit. Vancouver, British Columbia: D‐Wave Systems Inc., 2019.

Farhi, E, Goldstone, J, Gutmann, S, Sipser, M. Quantum computation by adiabatic evolution. *arXiv preprint quant‐ph/0001106*; 2000.

Born, M, Fock, V. Beweis des Adiabatensatzes. Z Phys. 1928;51(3–4):165–180.

Van Dam, W, Mosca, M, Vazirani, U. How powerful is adiabatic quantum computation? Proceedings 42nd IEEE Symposium on Foundations of Computer Science. New York: IEEE, 2001; p. 279–287.

van Dam, W, Vazirani, U. Limits on quantum adiabatic optimization. Unpublished manuscript, https://people.eecs.berkeley.edu/∼vazirani/pubs/qao.pdf; 2001.

Reichardt, BW. The quantum adiabatic optimization algorithm and local minima. Proceedings of the Thirty‐Sixth Annual ACM Symposium on Theory of Computing. New York: ACM, 2004; p. 502–510.

Farhi, E, Goldstone, J, Gutmann, S, Lapan, J, Lundgren, A, Preda, D. A quantum adiabatic evolution algorithm applied to random instances of an NP‐complete problem. Science. 2001;292(5516):472–475.

Aharonov, D, Van Dam, W, Kempe, J, Landau, Z, Lloyd, S, Regev, O. Adiabatic quantum computation is equivalent to standard quantum computation. SIAM Rev. 2008;50(4):755–787.

Albash, T, Lidar, DA. Demonstration of a scaling advantage for a quantum annealer over simulated annealing. Phys Rev X. 2018;8(3):031016.

Farhi, E, Goldstone, J, Gutmann, S. A quantum approximate optimization algorithm. *arXiv preprint arXiv:1411.4028*; 2014.

Hadfield, S, Wang, Z, O`Gorman, B, Rieffel, EG, Venturelli, D, Biswas, R. From the quantum approximate optimization algorithm to a quantum alternating operator ansatz. Algorithms. 2019;12(2):34.

Hadfield, S, Wang, Z, Rieffel, EG, O`Gorman, B, Venturelli, D, Biswas, R. Quantum approximate optimization with hard and soft constraints. Proceedings of the Second International Workshop on Post Moores Era Supercomputing. New York: ACM, 2017; p. 15–21.

Google, AI, Quantum and Collaborators. Quantum approximate optimization of non‐planar graph problems on a planar superconducting processor. *arXiv preprint arXiv:2004.04197*; 2020.

Dill, KA. Theory for the folding and stability of globular proteins. Biochemistry. 1985;24(6):1501–1509.

Robert, S, Barkoutsos, PK, Woerner, S, Tavernelli, I. Resource‐efficient quantum algorithm for protein folding. *arXiv preprint arXiv:1908.02163*; 2019.

Lau, KF, Dill, KA. A lattice statistical mechanics model of the conformational and sequence spaces of proteins. Macromolecules. 1989;22(10):3986–3997.

Miyazawa, S, Jernigan, RL. Estimation of effective interresidue contact energies from protein crystal structures: Quasi‐chemical approximation. Macromolecules. 1985;18(3):534–552.

Dill, KA, Bromberg, S, Yue, K, et al. Principles of protein folding—A perspective from simple exact models. Protein Sci. 1995;4(4):561–602.

Skolnick, J, Kolinski, A, Kihara, D, Betancourt, M, Rotkiewicz, P, Boniecki, M. Ab initio protein structure prediction via a combination of threading, lattice folding, clustering, and structure refinement. Proteins Struct Funct Bioinform. 2001;45(S5):149–156.

Hoque, T, Chetty, M, Sattar, A. Extended HP model for protein structure prediction. J Comput Biol. 2009;16(1):85–103.

Fingerhuth, M, Babej, T, Ing, C. A quantum alternating operator ansatz with hard and soft constraints for lattice protein folding. *arXiv preprint arXiv:1810.13411*, 2018.

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. 2008;78(1):012320.

Babbush, R, Perdomo‐Ortiz, A, O`Gorman, B, Macready, W, Aspuru‐Guzik, A. Construction of energy functions for lattice heteropolymer models: A case study in constraint satisfaction programming and adiabatic quantum optimization. *arXiv preprint arXiv:1211.3422*; 2012.

Perdomo‐Ortiz, A, Dickson, N, Drew‐Brook, M, Rose, G, Aspuru‐Guzik, A. Finding low‐energy conformations of lattice protein models by quantum annealing. Sci Rep. 2012;2:571.

Babej, T, Ing, C, Fingerhuth, M. Coarse‐grained lattice protein folding on a quantum annealer. *arXiv preprint arXiv:1811.00713*; 2018.

Hart, WE, Istrail, S. Robust proofs of NP‐hardness for protein folding: General lattices and energy potentials. J Comput Biol. 1997;4(1):1–22.

Berger, B, Leighton, T. Protein folding in the hydrophobic‐hydrophilic (HP) model is NP‐complete. J Comput Biol. 1998;5(1):27–40.

Aaronson, S. Guest column: NP‐complete problems and physical reality. ACM Sigact News. 2005;36(1):30–52.

Rønnow, TF, Wang, Z, Job, J, et al. Defining and detecting quantum speedup. Science. 2014;345(6195):420–424.

Outeiral, C, Morris, GM, Shi, J, Strahm, M, Benjamin, SC, Deane, CM. Investigating the potential for a limited quantum speedup on protein lattice problems. *arXiv preprint arXiv:2004.01118*; 2020

Mulligan, VK, Melo, H, Merritt, HI, et al. Designing peptides on a quantum computer. bioRxiv. 2019;752485. https://doi.org/10.1101/752485.

Rohl, CA, Strauss, CE, Misura, KM, Baker, D. Protein structure prediction using Rosetta. Methods in enzymology. Volume 383. Amsterdam: Elsevier, 2004; p. 66–93.

Marchand, D, Noori, M, Roberts, A, et al. A variable neighbourhood descent heuristic for conformational search using a quantum annealer. Sci Rep. 2019;9(1):1–13.