Finding low-energy conformations of lattice protein models by quantum annealing
Open accessAbstract
Lattice protein folding models are a cornerstone of computational biophysics. Although these models are a coarse grained representation, they provide useful insight into the energy landscape of natural proteins. Finding low-energy threedimensional structures is an intractable problem even in the simplest model, the Hydrophobic-Polar (HP) model. Description of protein-like properties are more accurately described by generalized models, such as the one proposed by Miyazawa and Jernigan (MJ), which explicitly take into account the unique interactions among all 20 amino acids. There is theoretical and experimental evidence of the advantage of solving classical optimization problems using quantum annealing over its classical analogue (simulated annealing). In this report, we present a benchmark implementation of quantum annealing for lattice protein folding problems (six different experiments up to 81 superconducting quantum bits). This first implementation of a biophysical problem paves the way towards studying optimization problems in biophysics and statistical mechanics using quantum devices.
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Perdomo-Ortiz et al. used quantum annealing to find low-energy conformations of different lattice protein models.
“Perdomo-Ortiz etal. used quantum annealing to find low-energy conformations of different lattice protein models”
From Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics (2024) · cites this paper as [81]
In Perdomo-Ortiz et al. 2012, authors use five and eight qubits for the four-amino-acid sequence to encode and solve the protein folding problem for a short tetrapeptide and hexapeptide chain.
“In (Perdomo-Ortiz et al. , 2012), authors use five and eight qubits for the four-amino-acid sequence to encode and solve the protein folding problem for a short tetrapeptide and hexapeptide chain.”
From Noisy intermediate-scale quantum algorithms (2022) · cites this paper as Perdomo-Ortiz et al., 2012
The development of software platforms such as Rigetti's pyQuil, IBM's QISKit, ETH Zürich's ProjectQ, Google's Cirq, and Xanadu's Strawberry Fields has amplified the synergy between quantum algorithm developers and quantum machines.
“Fortunately, this synergy has been amplified by the development of software platforms such as Rigetti's pyQuil 389 , IBM's QISKit 390 , ETH Z¨ urich's ProjectQ 391 , Google's Cirq 392 , and Xanadu's Strawberry Fields 393 .”
From Quantum Chemistry in the Age of Quantum Computing (2019) · cites this paper as [391]
The first attempts to solve protein folding with quantum annealing and later hybrid algorithms based on QAOA, VQE, and quantum random walks have been proposed.
“The first attempts to solve the protein folding optimization problem relied on quantum annealing [205,206] while, more recently, hybrid classical-variational algorithms based on QAOA, [109] VQE, [110] and on quantum random walks [207] have been proposed.”
From Quantum Computing for Molecular Biology** (2023) · cites this paper as [205]
Quantum annealing has been applied to the protein folding problem using simplified lattice models for the polypeptide chain.
“Quantum annealing has been applied to the protein folding problem [71, 72], using simplified lattice models for the polypeptide chain.”
From Quantum computing algorithms: getting closer to critical problems in computational biology (2022) · cites this paper as [71]
It is currently estimated that quantum advantage will be achieved by quantum computers with around 10^6-10^7 physical qubits.
“it is currently estimated that quantum advantage, i.e. runtimes faster than those of classical computations for specific problems (such as the ones described below), will be achieved by quantum computers with around 106–107 physical qubits [71, 72]”
From Quantum computing algorithms: getting closer to critical problems in computational biology (2022) · cites this paper as [71]
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Cites
Optimization by Simulated Annealing
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983) · Science · doi:10.1126/science.220.4598.671 · Cited by 45,134 · see citing works
Principles that Govern the Folding of Protein Chains
Anfinsen, C. B. (1973) · Science · doi:10.1126/science.181.4096.223 · Cited by 7,096 · see citing works
Quantum annealing in the transverse Ising model
Kadowaki, T., & Nishimori, H. (1998) · Physical Review E · doi:10.1103/physreve.58.5355 · Cited by 2,339 · see citing works
A Quantum Adiabatic Evolution Algorithm Applied to Random Instances of an NP-Complete Problem
Farhi, E., Goldstone, J., Gutmann, S., Lapan, J., Lundgren, A., & Preda, D. (2001) · Science · doi:10.1126/science.1057726 · Cited by 2,056 · see citing works
Atomic-level characterization of the structural dynamics of proteins
Shaw, D. E., Maragakis, P., Lindorff-Larsen, K., Piana, S., Dror, R. O., Eastwood, M. P., Bank, J. A., Jumper, J. M., Salmon, J. K., Shan, Y., et al. (2010) · Science · doi:10.1126/science.1187409 · Cited by 1,923 · see citing works
Quantum annealing with manufactured spins
Johnson, M. W., Amin, M. H. S., Gildert, S., Lanting, T., Hamze, F., Dickson, N., Harris, R., Berkley, A. J., Johansson, J., Bunyk, P., et al. (2011) · Nature · doi:10.1038/nature10012 · Cited by 1,891 · see citing works
Self-citationManipulating the Quantum State of an Electrical Circuit
Vion, D., Aassime, A., Cottet, A., Joyez, P., Pothier, H., Urbina, C., Esteve, D., & Devoret, M. (2002) · Science · doi:10.1126/science.1069372 · Cited by 1,624 · see citing works
On the computational complexity of Ising spin glass models
Barahona, F. (1982) · Journal of Physics A Mathematical and General · doi:10.1088/0305-4470/15/10/028 · Cited by 1,303 · see citing works
Miyazawa, S., & Jernigan, R. L. (1996) · Journal of Molecular Biology · doi:10.1006/jmbi.1996.0114 · Cited by 1,231 · see citing works
Dill, K. A., Ozkan, S. B., Shell, M. S., & Weikl, T. R. (2008) · Annual Review of Biophysics · doi:10.1146/annurev.biophys.37.092707.153558 · Cited by 1,163 · see citing works
Demonstration of two-qubit algorithms with a superconducting quantum processor
DiCarlo, L., Chow, J. M., Gambetta, J., Bishop, L. S., Johnson, B., Schuster, D., Majer, J., Blais, A., Frunzio, L., Girvin, S. M., et al. (2009) · Nature · doi:10.1038/nature08121 · Cited by 1,158 · see citing works
Superconducting Circuits and Quantum Information
You, J. Q., & Nori, F. (2005) · Physics Today · doi:10.1063/1.2155757 · Cited by 1,002 · see citing works
A lattice statistical mechanics model of the conformational and sequence spaces of proteins
Lau, K. F., & Dill, K. A. (1989) · Macromolecules · doi:10.1021/ma00200a030 · Cited by 954 · see citing works
Šali, A., Shakhnovich, E. I., & Karplus, M. (1994) · Nature · doi:10.1038/369248a0 · Cited by 944 · see citing works
Synthesizing arbitrary quantum states in a superconducting resonator
Hofheinz, M., Wang, H., Ansmann, M., Bialczak, R. C., Lucero, E. A., Neeley, M., O’Connell, A. D., Sank, D., Wenner, J., Martinis, J. M., et al. (2009) · Nature · doi:10.1038/nature08005 · Cited by 875 · see citing works
Theory of Quantum Annealing of an Ising Spin Glass
Santoro, G. E., Martoňák, R., Tosatti, E., & Car, R. (2002) · Science · doi:10.1126/science.1068774 · Cited by 734 · see citing works
<i>Colloquium</i> : Quantum annealing and analog quantum computation
Das, A., & Chakrabarti, B. K. (2008) · Reviews of Modern Physics · doi:10.1103/revmodphys.80.1061 · Cited by 700 · see citing works
Quantum annealing: A new method for minimizing multidimensional functions
Finnila, A. B., Gomez, M. A., Sebenik, C., Stenson, C., & Doll, J. D. (1994) · Chemical Physics Letters · doi:10.1016/0009-2614(94)00117-0 · Cited by 698 · see citing works
Superconducting Circuits and Quantum Information
You, J. Q., & Nori, F. (2006) · CERN Document Server (European Organization for Nuclear Research) · Cited by 613 · see citing works
Preparation and measurement of three-qubit entanglement in a superconducting circuit
DiCarlo, L., Reed, M. D., Sun, L., Johnson, B., Chow, J. M., Gambetta, J., Frunzio, L., Girvin, S. M., Devoret, M., & Schoelkopf, R. J. (2010) · Nature · doi:10.1038/nature09416 · Cited by 568 · see citing works
Quantum Computation by Adiabatic Evolution
Farhi, E., Goldstone, J., Gutmann, S., & Sipser, M. (2000) · arXiv · doi:10.48550/arxiv.quant-ph/0001106 · Cited by 536 · see citing works
Quantum Annealing of a Disordered Magnet
Brooke, J., Bitko, D., F., T., Rosenbaum, & Aeppli, G. (1999) · Science · doi:10.1126/science.284.5415.779 · Cited by 518 · see citing works
Protein Folding in the Hydrophobic-Hydrophilic ( HP ) Model is NP-Complete
Berger, B., & Leighton, T. (1998) · Journal of Computational Biology · doi:10.1089/cmb.1998.5.27 · Cited by 421 · see citing works
Generation of three-qubit entangled states using superconducting phase qubits
Neeley, M., Bialczak, R. C., Lenander, M., Lucero, E. A., Mariantoni, M., O’Connell, A. D., Sank, D., Wang, H., Weides, M. P., Wenner, J., et al. (2010) · Nature · doi:10.1038/nature09418 · Cited by 415 · see citing works
Proteins with selected sequences fold into unique native conformation
Shakhnovich, E. I. (1994) · Physical Review Letters · doi:10.1103/physrevlett.72.3907 · Cited by 393 · see citing works
On the Complexity of Protein Folding
Crescenzi, P., Goldman, D., Papadimitriou, C. H., Piccolboni, A., & Yannakakis, M. (1998) · Journal of Computational Biology · doi:10.1089/cmb.1998.5.423 · Cited by 363 · see citing works
Optimization using quantum mechanics: quantum annealing through adiabatic evolution
Santoro, G. E., & Tosatti, E. (2006) · Journal of Physics A: Mathematical and General · doi:10.1088/0305-4470/39/36/r01 · Cited by 354 · see citing works
Protein Folding Theory: From Lattice to All-Atom Models
Mirny, L., & Shakhnovich, E. (2001) · Annual Review of Biophysics and Biomolecular Structure · doi:10.1146/annurev.biophys.30.1.361 · Cited by 341 · see citing works
Heteropolymer freezing and design: Towards physical models of protein folding
Pande, V. S., Grosberg, A. Y., & Tanaka, T. (2000) · Reviews of Modern Physics · doi:10.1103/revmodphys.72.259 · Cited by 284 · see citing works
Experimental investigation of an eight-qubit unit cell in a superconducting optimization processor
Harris, R., Johnson, M. W., Lanting, T. M., Berkley, A. J., Johansson, J., Bunyk, P., Tolkacheva, E., Ladizinsky, E., Ladizinsky, N., Oh, T., et al. (2010) · Physical Review B · doi:10.1103/physrevb.82.024511 · Cited by 268 · see citing works
Self-citationQuantum non-demolition measurement of a superconducting two-level system
Lupaşcu, A., Saito, S., Picot, T., de Groot, P. C., Harmans, C. J. P. M., & Mooij, J. E. (2007) · Nature Physics · doi:10.1038/nphys509 · Cited by 251 · see citing works
New Optimization Algorithms in Physics
Hartmann, A. K., & Rieger, H. (2004) · Wiley eBooks · doi:10.1002/3527603794 · Cited by 244 · see citing works
Ray, P., Chakrabarti, B. K., & Chakrabarti, A. (1989) · Physical review. B, Condensed matter · doi:10.1103/physrevb.39.11828 · Cited by 209 · see citing works
Forces of tertiary structural organization in globular proteins.
Yue, K., & Dill, K. A. (1995) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.92.1.146 · Cited by 191 · see citing works
Quantum Factorization of 143 on a Dipolar-Coupling Nuclear Magnetic Resonance System
Xu, N., Zhu, J., Lu, D., Zhou, X., Peng, X., & Du, J. (2012) · Physical Review Letters · doi:10.1103/physrevlett.108.130501 · Cited by 182 · see citing works
Experimental Implementation of an Adiabatic Quantum Optimization Algorithm
Steffen, M., van Dam, W., Hogg, T., Breyta, G., & Chuang, I. L. (2003) · Physical Review Letters · doi:10.1103/physrevlett.90.067903 · Cited by 180 · see citing works
A scalable control system for a superconducting adiabatic quantum optimization processor
Johnson, M. W., Bunyk, P., Maibaum, F., Tolkacheva, E., Berkley, A. J., Chapple, E. M., Harris, R., Johansson, J., Lanting, T. M., Perminov, I., et al. (2010) · Superconductor Science and Technology · doi:10.1088/0953-2048/23/6/065004 · Cited by 162 · see citing works
Self-citationRobust Proofs of NP-Hardness for Protein Folding: General Lattices and Energy Potentials
Hart, W. E., & Istrail, S. (1997) · Journal of Computational Biology · doi:10.1089/cmb.1997.4.1 · Cited by 139 · see citing works
Minimum energy compact structures of random sequences of heteropolymers
Camacho, C. J., & Thirumalai, D. (1993) · Physical Review Letters · doi:10.1103/physrevlett.71.2505 · Cited by 134 · see citing works
Amara, P., Hsu, D., & Straub, J. E. (1993) · The Journal of Physical Chemistry · doi:10.1021/j100127a023 · Cited by 120 · see citing works
Perdomo, A., Truncik, C., Tubert-Brohman, I., Rose, G., & Aspuru-Guzik, A. (2008) · Physical Review A · doi:10.1103/physreva.78.012320 · Cited by 120 · see citing works
Self-citationFactors Governing Fibrillogenesis of Polypeptide Chains Revealed by Lattice Models
Li, M. S., Co, N. T., Reddy, G., Hu, C., Straub, J. E., & Thirumalai, D. (2010) · Physical Review Letters · doi:10.1103/physrevlett.105.218101 · Cited by 117 · see citing works
A scalable readout system for a superconducting adiabatic quantum optimization system
Berkley, A. J., Johnson, M. W., Bunyk, P., Harris, R., Johansson, J., Lanting, T. M., Ladizinsky, E., Tolkacheva, E., Amin, M. H. S., & Rose, G. (2010) · Superconductor Science and Technology · doi:10.1088/0953-2048/23/10/105014 · Cited by 110 · see citing works
Self-citationExperimental Determination of Ramsey Numbers
Bian, Z., Chudak, F. A., Macready, W. G., Clark, L., & Gaitan, F. (2013) · Physical Review Letters · doi:10.1103/physrevlett.111.130505 · Cited by 101 · see citing works
New Optimization Algorithms in Physics
Hartmann, A. K., & Rieger, H. (2004) · CERN Document Server (European Organization for Nuclear Research) · Cited by 97 · see citing works
Adiabatic quantum computing for random satisfiability problems
Hogg, T. (2003) · Physical Review A · doi:10.1103/physreva.67.022314 · Cited by 93 · see citing works
Decoherence in adiabatic quantum computation
Amin, M. H. S., Averin, D. V., & Nesteroff, J. A. (2009) · Physical Review A · doi:10.1103/physreva.79.022107 · Cited by 88 · see citing works
Lattice Models of Protein Folding, Dynamics and Thermodynamics
Koliński, A., & Skolnick, J. (1996) · Cited by 86 · see citing works
An Efficient Computational Method for Globally Optimal Threading1
Xu, Y., Xu, D. H., & Uberbacher, E. C. (1998) · Journal of Computational Biology · doi:10.1089/cmb.1998.5.597 · Cited by 82 · see citing works
Quantum and Classical Glass Transitions in LiHoxY1−xF4
Ancona-Torres, C., Silevitch, D. M., Aeppli, G., & Rosenbaum, T. F. (2008) · Physical Review Letters · doi:10.1103/physrevlett.101.057201 · Cited by 73 · see citing works
Geometrical dependence of the low-frequency noise in superconducting flux qubits
Lanting, T. M., Berkley, A. J., Bumble, B., Bunyk, P., Fung, A., Johansson, J., Kaul, A. B., Kleinsasser, A. W., Ladizinsky, E., Maibaum, F., et al. (2009) · Physical Review B · doi:10.1103/physrevb.79.060509 · Cited by 66 · see citing works
Probing noise in flux qubits via macroscopic resonant tunneling.
Harris, R., Johnson, M. W., Han, S.-Y., Berkley, A., Johansson, J., Bunyk, P., Ladizinsky, E., Govorkov, S., Thom, M., Uchaikin, S., et al. (2007) · Physical Review Letters · doi:10.1103/physrevlett.101.117003 · Cited by 65 · see citing works
Role of single-qubit decoherence time in adiabatic quantum computation
Amin, M. H. S., Truncik, C. J. S., & Averin, D. V. (2009) · Physical Review A · doi:10.1103/physreva.80.022303 · Cited by 63 · see citing works
Scalable Architecture for Adiabatic Quantum Computing of Np-Hard Problems
Kaminsky, W. M., & Lloyd, S. (2004) · Springer eBooks · doi:10.1007/978-1-4419-9092-1_25 · Cited by 58 · see citing works
Molecular nanomagnets: towards molecular spintronics
Wernsdorfer, W. (2010) · International Journal of Nanotechnology · doi:10.1504/ijnt.2010.031732 · Cited by 56 · see citing works
Scalable Superconducting Architecture for Adiabatic Quantum ComputationPreprint
Kaminsky, W. M., Lloyd, S., & Orlando, T. P. (2004) · arXiv (Cornell University) · doi:10.48550/arxiv.quant-ph/0403090 · Cited by 35 · see citing works
Simple Theory of Protein Folding Kinetics
Pande, V. S. (2010) · Physical Review Letters · doi:10.1103/physrevlett.105.198101 · Cited by 26 · see citing works
Bohannon, J. (2005) · Science · doi:10.1126/science.308.5723.810 · Cited by 22 · see citing works
Probing high-frequency noise with macroscopic resonant tunneling
Lanting, T. M., Amin, M. H. S., Johnson, M. W., Altomare, F., Berkley, A. J., Gildert, S., Harris, R., Johansson, J., Bunyk, P., Ladizinsky, E., et al. (2011) · Physical Review B · doi:10.1103/physrevb.83.180502 · Cited by 22 · see citing works
Synchronization of multiple coupled rf-SQUID flux qubits
Harris, R., Brito, F., Berkley, A. J., Johansson, J., Johnson, M. W., Lanting, T., Bunyk, P., Ladizinsky, E., Bumble, B., Fung, A., et al. (2009) · New Journal of Physics · doi:10.1088/1367-2630/11/12/123022 · Cited by 17 · see citing works
Experimental determination of Ramsey numbers with quantum annealingPreprint
Bian, Z., Chudak, F. A., Macready, W. G., et al. (2012) · arXiv (Cornell University) · Cited by 12 · see citing works
Jönsson, P. E., Mathieu, R., Wernsdorfer, W., Tkachuk, A. M., & Barbara, B. (2008) · arXiv (Cornell University) · doi:10.48550/arxiv.0803.1357 · Cited by 10 · see citing works
Implementation of a Quantum Annealing Algorithm Using a Superconducting CircuitPreprint
Harris, R., Berkley, A. J., Johansson, J., Johnson, M. W., Lanting, T. M., Bunyk, P., Tolkacheva, E., Ladizinsky, E., Bumble, B., Fung, A., et al. (2009) · arXiv (Cornell University) · doi:10.48550/arxiv.0903.3906 · Cited by 10 · see citing works
Xu, N., Zhu, J., Lu, D., Zhou, X., Peng, X., & Du, J. (2012) · Physical Review Letters · doi:10.1103/physrevlett.109.269902 · Cited by 7 · see citing works
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Ising formulations of many NP problems
Lucas, A. (2014) · Frontiers in Physics · doi:10.3389/fphy.2014.00005 · Cited by 2,512 · see citing works
Noisy intermediate-scale quantum algorithms
Bharti, K., Cervera-Lierta, A., Kyaw, T. H., Haug, T., Alperin-Lea, S., Anand, A., Degroote, M., Heimonen, H., Kottmann, J. S., Menke, T., et al. (2022) · Reviews of Modern Physics · doi:10.1103/revmodphys.94.015004 · Cited by 1,726 · see citing works
Self-citationQuantum computational chemistry
McArdle, S., Endo, S., Aspuru-Guzik, A., Benjamin, S. C., & Yuan, X. (2020) · Reviews of Modern Physics · doi:10.1103/revmodphys.92.015003 · Cited by 1,621 · see citing works
Self-citationQuantum Chemistry in the Age of Quantum Computing
Cao, Y., Romero, J., Olson, J. P., Degroote, M., Johnson, P. D., Kieferová, M., Kivlichan, I. D., Menke, T., Peropadre, B., Sawaya, N. P. D., et al. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00803 · Cited by 1,560 · see citing works
Self-citationQuantum information processing with superconducting circuits: a review
Wendin, G. P. (2017) · Reports on Progress in Physics · doi:10.1088/1361-6633/aa7e1a · Cited by 970 · see citing works
Evidence for quantum annealing with more than one hundred qubits
Boixo, S., Rønnow, T. F., Isakov, S. V., Wang, Z., Wecker, D., Lidar, D. A., Martinis, J. M., & Troyer, M. (2014) · Nature Physics · doi:10.1038/nphys2900 · Cited by 810 · see citing works
Reinforcement learning for combinatorial optimization: A survey
Mazyavkina, N., Sviridov, S., Ivanov, S., & Burnaev, E. V. (2021) · Computers & Operations Research · doi:10.1016/j.cor.2021.105400 · Cited by 762 · see citing works
A fully programmable 100-spin coherent Ising machine with all-to-all connections
McMahon, P. L., Marandi, A., Haribara, Y., Hamerly, R., Langrock, C., Tamate, S., Inagaki, T., Takesue, H., Utsunomiya, S., Aihara, K., et al. (2016) · Science · doi:10.1126/science.aah5178 · Cited by 700 · see citing works
Perspectives of quantum annealing: methods and implementations
Hauke, P., Katzgraber, H. G., Lechner, W., Nishimori, H., & Oliver, W. D. (2020) · Reports on Progress in Physics · doi:10.1088/1361-6633/ab85b8 · Cited by 507 · see citing works
Physics-Inspired Optimization for Quadratic Unconstrained Problems Using a Digital Annealer
Aramon, M., Rosenberg, G., Valiante, E., Miyazawa, T., Tamura, H., & Katzgraber, H. G. (2019) · Frontiers in Physics · doi:10.3389/fphy.2019.00048 · Cited by 442 · see citing works
Potential of quantum computing for drug discovery
Cao, Y., Romero, J., & Aspuru-Guzik, A. (2018) · IBM Journal of Research and Development · doi:10.1147/jrd.2018.2888987 · Cited by 357 · see citing works
Self-citationQuantum annealing for industry applications: introduction and review
Yarkoni, S., Raponi, E., Bäck, T., & Schmitt, S. (2022) · Reports on Progress in Physics · doi:10.1088/1361-6633/ac8c54 · Cited by 341 · see citing works
Entanglement in a Quantum Annealing Processor
Lanting, T., Przybysz, A. J., Smirnov, A. Y., Spedalieri, F. M., Amin, M. H., Berkley, A. J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al. (2014) · Physical Review X · doi:10.1103/physrevx.4.021041 · Cited by 303 · see citing works
Self-citationExperimental signature of programmable quantum annealing
Boixo, S., Albash, T., Spedalieri, F. M., Chancellor, N., & Lidar, D. A. (2013) · Nature Communications · doi:10.1038/ncomms3067 · Cited by 286 · see citing works
Adiabatic Quantum Computation and Quantum Annealing
McGeoch, C. C. (2014) · Synthesis lectures on quantum computing · doi:10.2200/s00585ed1v01y201407qmc008 · Cited by 270 · see citing works
A quantum annealing architecture with all-to-all connectivity from local interactions
Lechner, W., Hauke, P., & Zoller, P. (2015) · Science Advances · doi:10.1126/sciadv.1500838 · Cited by 256 · see citing works
Intrinsic optimization using stochastic nanomagnets
Sutton, B., Camsari, K. Y., Behin‐Aein, B., & Datta, S. (2017) · Scientific Reports · doi:10.1038/srep44370 · Cited by 245 · see citing works
The prospects of quantum computing in computational molecular biology
Outeiral, C., Strahm, M., Shi, J., Morris, G. M., Benjamin, S. C., & Deane, C. M. (2021) · WIREs Computational Molecular Science · doi:10.1002/wcms.1481 · Cited by 232 · see citing works
A case study in programming a quantum annealer for hard operational planning problems
Rieffel, E. G., Venturelli, D., O’Gorman, B., Do, M. B., Prystay, E. M., & Smelyanskiy, V. (2014) · Quantum Information Processing · doi:10.1007/s11128-014-0892-x · Cited by 221 · see citing works
Benedetti, M., Realpe-Gómez, J., Biswas, R., & Perdomo-Ortiz, A. (2016) · Physical Review A · doi:10.1103/physreva.94.022308 · Cited by 215 · see citing works
Self-citationQuantum Optimization of Fully Connected Spin Glasses
Venturelli, D., Mandrà, S., Knysh, S., O’Gorman, B., Biswas, R., & Smelyanskiy, V. (2015) · Physical Review X · doi:10.1103/physrevx.5.031040 · Cited by 209 · see citing works
Resource-efficient quantum algorithm for protein folding
Robert, A., Barkoutsos, P. K., Woerner, S., & Tavernelli, I. (2021) · npj Quantum Information · doi:10.1038/s41534-021-00368-4 · Cited by 201 · see citing works
Adiabatic Quantum Simulation of Quantum Chemistry
Babbush, R., Love, P. J., & Aspuru-Guzik, A. (2014) · Scientific Reports · doi:10.1038/srep06603 · Cited by 191 · see citing works
Self-citationAjagekar, A., Humble, T., & You, F. (2020) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2019.106630 · Cited by 191 · see citing works
“Four applications are considered in this paper, namely the molecular conformation problem [39], the job-shop scheduling problem [40], the manufacturing cell formation problem and the vehicle routing problem [41].”
BackgroundQuantum annealing: an overview
Rajak, A., Suzuki, S., Dutta, A., & Chakrabarti, B. K. (2022) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2021.0417 · Cited by 182 · see citing works
Designing metamaterials with quantum annealing and factorization machines
Kitai, K., Guo, J., Ju, S., Tanaka, S., Tsuda, K., Shiomi, J., & Tamura, R. (2020) · Physical Review Research · doi:10.1103/physrevresearch.2.013319 · Cited by 181 · see citing works
Forging Solid-State Qubit Design Principles in a Molecular Furnace
Graham, M. J., Zadrozny, J. M., Fataftah, M. S., & Freedman, D. E. (2017) · Chemistry of Materials · doi:10.1021/acs.chemmater.6b05433 · Cited by 174 · see citing works
Exploratory Combinatorial Optimization with Reinforcement Learning
Barrett, T. D., Clements, W. R., Foerster, J., & Lvovsky, A. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.5723 · Cited by 172 · see citing works
Quantum computing at the frontiers of biological sciences
Emani, P. S., Warrell, J., Anticevic, A., Bekiranov, S., Gandal, M., McConnell, M. J., Sapiro, G., Aspuru-Guzik, A., Baker, J. T., Bastiani, M., et al. (2021) · Nature Methods · doi:10.1038/s41592-020-01004-3 · Cited by 163 · see citing works
Self-citationExperimental evaluation of an adiabiatic quantum system for combinatorial optimization
McGeoch, C. C., & Wang, C. (2013) · ACM International Conference on Computing Frontiers (CF) · doi:10.1145/2482767.2482797 · Cited by 163 · see citing works
Quantum annealing with all-to-all connected nonlinear oscillators
Puri, S., Andersen, C. K., Grimsmo, A. L., & Blais, A. (2017) · Nature Communications · doi:10.1038/ncomms15785 · Cited by 162 · see citing works
Quantum machine learning for chemistry and physics
Sajjan, M., Li, J., Selvarajan, R., Sureshbabu, S. H., Kale, S. S., Gupta, R., Singh, V., & Kais, S. (2022) · Chemical Society Reviews · doi:10.1039/d2cs00203e · Cited by 157 · see citing works
Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models
Benedetti, M., Realpe-Gómez, J., Biswas, R., & Perdomo‐Ortiz, A. (2017) · Physical Review X · doi:10.1103/physrevx.7.041052 · Cited by 155 · see citing works
Self-citationQuantum Computing and Simulations for Energy Applications: Review and Perspective
Paudel, H. P., Syamlal, M., Crawford, S. E., Lee, Y., Shugayev, R., Lu, P., Ohodnicki, P. R., Mollot, D., & Duan, Y. (2022) · ACS Engineering Au · doi:10.1021/acsengineeringau.1c00033 · Cited by 132 · see citing works
Superconducting Circuit Companion—an Introduction with Worked Examples
Rasmussen, S. E., Christensen, K. S., Pedersen, S. P., Kristensen, L. B., Bækkegaard, T., Loft, N. J. S., & Zinner, N. T. (2021) · PRX Quantum · doi:10.1103/prxquantum.2.040204 · Cited by 128 · see citing works
Exploiting Locality in Quantum Computation for Quantum Chemistry
McClean, J. R., Babbush, R., Love, P. J., & Aspuru-Guzik, A. (2014) · The Journal of Physical Chemistry Letters · doi:10.1021/jz501649m · Cited by 125 · see citing works
Self-citationMultiple Quantum Coherences from Hyperfine Transitions in a Vanadium(IV) Complex
Zadrozny, J. M., Niklas, J., Poluektov, O. G., & Freedman, D. E. (2014) · Journal of the American Chemical Society · doi:10.1021/ja507846k · Cited by 120 · see citing works
Quantum Navigation and Ranking in Complex Networks
Sánchez-Burillo, E., Duch, J., Gómez-Gardeñes, J., & Zueco, D. (2012) · Scientific Reports · doi:10.1038/srep00605 · Cited by 115 · see citing works
Quantum Computation Based on Quantum Adiabatic Bifurcations of Kerr-Nonlinear Parametric Oscillators
Goto, H. (2019) · Journal of the Physical Society of Japan · doi:10.7566/jpsj.88.061015 · Cited by 114 · see citing works
Quantum computing based hybrid deep learning for fault diagnosis in electrical power systems
Ajagekar, A., & You, F. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.117628 · Cited by 113 · see citing works
Enhanced sampling techniques in biomolecular simulations
Spiwok, V., Sucur, Z., & Hosek, P. (2014) · Biotechnology Advances · doi:10.1016/j.biotechadv.2014.11.011 · Cited by 110 · see citing works
Control of Automated Guided Vehicles Without Collision by Quantum Annealer and Digital Devices
Ohzeki, M., Miki, A., Miyama, M. J., & Terabe, M. (2019) · Frontiers in Computer Science · doi:10.3389/fcomp.2019.00009 · Cited by 104 · see citing works
Quantum computing for near-term applications in generative chemistry and drug discovery.
Pyrkov, A., Aliper, A., Bezrukov, D., Lin, Y.-C., Polykovskiy, D., Kamya, P., Ren, F., & Zhavoronkov, A. (2023) · Drug Discovery Today · doi:10.1016/j.drudis.2023.103675 · Cited by 100 · see citing works
Adiabatic quantum programming: minor embedding with hard faults
Klymko, C., Sullivan, B. D., & Humble, T. S. (2014) · Quantum Information Processing · doi:10.1007/s11128-013-0683-9 · Cited by 99 · see citing works
“In this section, we review the hardware graph that has been the basis for several proposed or demonstrated experimental studies [4,14,18,22].”
MethodQuantum Computing for Molecular Biology**
Baiardi, A., Christandl, M., & Reiher, M. (2023) · ChemBioChem · doi:10.1002/cbic.202300120 · Cited by 93 · see citing works
Simultaneous Perturbation Stochastic Approximation of the Quantum Fisher Information
Gacon, J., Zoufal, C., Carleo, G., & Woerner, S. (2021) · Quantum · doi:10.22331/q-2021-10-20-567 · Cited by 93 · see citing works
Bayesian network structure learning using quantum annealing
O’Gorman, B., Babbush, R., Perdomo‐Ortiz, A., Aspuru‐Guzik, A., & Smelyanskiy, V. (2015) · The European Physical Journal Special Topics · doi:10.1140/epjst/e2015-02349-9 · Cited by 92 · see citing works
Self-citationA QUBO Model for the Traveling Salesman Problem with Time Windows
Papalitsas, C., Andronikos, T., Giannakis, K., Theocharopoulou, G., & Fanarioti, S. (2019) · Algorithms · doi:10.3390/a12110224 · Cited by 91 · see citing works
Adiabatic state preparation study of methylene
Veis, L., & Pittner, J. (2014) · The Journal of Chemical Physics · doi:10.1063/1.4880755 · Cited by 90 · see citing works
Annealing by simulating the coherent Ising machine
Tiunov, E. S., Ulanov, A. E., & Lvovsky, A. I. (2019) · Optics Express · doi:10.1364/oe.27.010288 · Cited by 90 · see citing works
Domain wall encoding of discrete variables for quantum annealing and QAOA
Chancellor, N. (2019) · Quantum Science and Technology · doi:10.1088/2058-9565/ab33c2 · Cited by 90 · see citing works
Biology and medicine in the landscape of quantum advantages
Cordier, B. A., Sawaya, N. P. D., Guerreschi, G. G., & McWeeney, S. K. (2022) · Journal of The Royal Society Interface · doi:10.1098/rsif.2022.0541 · Cited by 87 · see citing works
Scaling out Ising machines using a multi-chip architecture for simulated bifurcation
Tatsumura, K., Yamasaki, M., & Goto, H. (2021) · Nature Electronics · doi:10.1038/s41928-021-00546-4 · Cited by 87 · see citing works
Pal, S., Bhattacharya, M., Lee, S.-S., & Chakraborty, C. (2024) · Molecular Biotechnology · doi:10.1007/s12033-023-00765-4 · Cited by 84 · see citing works
“Perdomo–Ortiz et al. used quantum annealing to find low-energy conformations of different lattice protein models [81].”
Adiabatic evolution on a spatial-photonic Ising machine
Pierangeli, D., Marcucci, G., & Conti, C. (2020) · Optica · doi:10.1364/optica.398000 · Cited by 83 · see citing works
Multiple query optimization on the D-Wave 2X adiabatic quantum computer
Trummer, I., & Koch, C. E. (2016) · Proceedings of the VLDB Endowment · doi:10.14778/2947618.2947621 · Cited by 81 · see citing works
Quantum Machine Learning: Recent Advances, Challenges, and Perspectives
Lamichhane, P., & Rawat, D. B. (2025) · IEEE Access · doi:10.1109/access.2025.3573244 · Cited by 79 · see citing works
A quantum annealing approach for fault detection and diagnosis of graph-based systems
Perdomo‐Ortiz, A., Fluegemann, J., Narasimhan, S., Biswas, R., & Smelyanskiy, V. (2015) · The European Physical Journal Special Topics · doi:10.1140/epjst/e2015-02347-y · Cited by 78 · see citing works
Self-citationRecent Progress in Quantum Simulation Using Superconducting Circuits
Paraoanu, G. S. (2014) · Journal of Low Temperature Physics · doi:10.1007/s10909-014-1175-8 · Cited by 78 · see citing works
Annealing-Based Quantum Computing for Combinatorial Optimal Power Flow
Morstyn, T. (2022) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2022.3200590 · Cited by 77 · see citing works
Multivariable optimization: Quantum annealing and computation
Mukherjee, S., & Chakrabarti, B. K. (2015) · The European Physical Journal Special Topics · doi:10.1140/epjst/e2015-02339-y · Cited by 77 · see citing works
Ajagekar, A., & You, F. (2020) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2020.107119 · Cited by 75 · see citing works
Domain wall encoding of integer variables for quantum annealing and QAOA [dataset]
Chancellor, N. (2019) · Durham Research Online (Durham University) · doi:10.15128/r27d278t029 · Cited by 75 · see citing works
Forecasting financial crashes with quantum computing
Orús, R., Mugel, S., & Lizaso, E. (2019) · Physical Review A · doi:10.1103/physreva.99.060301 · Cited by 74 · see citing works
Chancellor, N., Zohren, S., & Warburton, P. A. (2017) · npj Quantum Information · doi:10.1038/s41534-017-0022-6 · Cited by 72 · see citing works
Haribara, Y., Utsunomiya, S., & Yamamoto, Y. (2016) · Entropy · doi:10.3390/e18040151 · Cited by 69 · see citing works
Prime factorization algorithm based on parameter optimization of Ising model
Wang, B., Hu, F., Yao, H., & Wang, C. (2020) · Scientific Reports · doi:10.1038/s41598-020-62802-5 · Cited by 69 · see citing works
Digitized Counterdiabatic Quantum Algorithm for Protein Folding
Chandarana, P., Hegade, N. N., Montalban, I., Solano, E., & Chen, X. (2023) · Physical Review Applied · doi:10.1103/physrevapplied.20.014024 · Cited by 68 · see citing works
A hybrid quantum computing pipeline for real world drug discovery
Li, W., Yin, Z., Li, X., Ma, D., Yi, S., Zhang, Z., Zou, C., Bu, K., Dai, M., Yue, J., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-67897-8 · Cited by 67 · see citing works
Breaking limitation of quantum annealer in solving optimization problems under constraints
Ohzeki, M. (2020) · Scientific Reports · doi:10.1038/s41598-020-60022-5 · Cited by 67 · see citing works
Benchmark of quantum-inspired heuristic solvers for quadratic unconstrained binary optimization
Oshiyama, H., & Ohzeki, M. (2022) · Scientific Reports · doi:10.1038/s41598-022-06070-5 · Cited by 67 · see citing works
Entanglement spectroscopy on a quantum computer
Johri, S., Steiger, D. S., & Troyer, M. (2017) · Physical review. B./Physical review. B · doi:10.1103/physrevb.96.195136 · Cited by 65 · see citing works
Engineering Reflective Metasurfaces With Ising Hamiltonian and Quantum Annealing
Ross, C., Gradoni, G., Lim, Q. J., & Peng, Z. (2021) · IEEE Transactions on Antennas and Propagation · doi:10.1109/tap.2021.3137424 · Cited by 64 · see citing works
Genome assembly using quantum and quantum-inspired annealing
Boev, A. S., Rakitko, A. S., Usmanov, S. R., Kobzeva, A. N., Popov, I. V., Ilinsky, V. V., Kiktenko, E. O., & Fedorov, A. K. (2021) · Scientific Reports · doi:10.1038/s41598-021-88321-5 · Cited by 63 · see citing works
Whitfield, J. D., Faccin, M., & Biamonte, J. (2012) · Europhysics Letters (EPL) · doi:10.1209/0295-5075/99/57004 · Cited by 63 · see citing works
Towards practical applications in quantum computational biology
Fedorov, A. K., & Gelfand, M. S. (2021) · Nature Computational Science · doi:10.1038/s43588-021-00024-z · Cited by 62 · see citing works
Readiness of Quantum Optimization Machines for Industrial Applications
Perdomo-Ortiz, A., Feldman, A., Ozaeta, A., Isakov, S. V., Zhu, Z., O’Gorman, B., Katzgraber, H. G., Diedrich, A., Neven, H., de Kleer, J., et al. (2019) · Physical Review Applied · doi:10.1103/physrevapplied.12.014004 · Cited by 62 · see citing works
Self-citationA transmon quantum annealer: decomposing many-body Ising constraints into pair interactions
Leib, M., Zoller, P., & Lechner, W. (2016) · Quantum Science and Technology · doi:10.1088/2058-9565/1/1/015008 · Cited by 62 · see citing works
Implementation of Quantum Annealing: A Systematic Review
Yulianti, L. P., & Surendro, K. (2022) · IEEE Access · doi:10.1109/access.2022.3188117 · Cited by 61 · see citing works
Item Listing Optimization for E-Commerce Websites Based on Diversity
Nishimura, N., Tanahashi, K., Suganuma, K., Miyama, M. J., & Ohzeki, M. (2019) · Frontiers in Computer Science · doi:10.3389/fcomp.2019.00002 · Cited by 61 · see citing works
A Perspective on Protein Structure Prediction Using Quantum Computers
Doğa, H., Raubenolt, B. A., Cumbo, F., Joshi, J., DiFilippo, F. P., Qin, J., Blankenberg, D. J., & Shehab, O. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c00067 · Cited by 59 · see citing works
Recent Advances in Quantum Computing for Drug Discovery and Development
Wang, P.-H., Chen, J.-H., Yang, Y.-Y., Lee, C., & Tseng, Y. J. (2023) · IEEE Nanotechnology Magazine · doi:10.1109/mnano.2023.3249499 · Cited by 58 · see citing works
Illa, M., & A, M. J. S. (2022) · Physical Review A · doi:10.1103/physreva.106.052605 · Cited by 58 · see citing works
Reinforcement Quantum Annealing: A Hybrid Quantum Learning Automata
Ayanzadeh, R., Halem, M., & Finin, T. (2020) · Scientific Reports · doi:10.1038/s41598-020-64078-1 · Cited by 57 · see citing works
Insights from incorporating quantum computing into drug design workflows
Lau, B., Emani, P. S., Chapman, J., Yao, L., Lam, T., Merrill, P., Warrell, J., Gerstein, M. B., & Lam, H. Y. K. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btac789 · Cited by 54 · see citing works
QFold: quantum walks and deep learning to solve protein folding
Casares, P. A. M., Campos, R., & Martin-Delgado, M. A. (2022) · Quantum Science and Technology · doi:10.1088/2058-9565/ac4f2f · Cited by 53 · see citing works
Opportunities and Challenges of Quantum Computing for Engineering Optimization
Wang, Y., Kim, J. E., & Suresh, K. (2023) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.4062969 · Cited by 53 · see citing works
Qubit Control Limited by Spin–Lattice Relaxation in a Nuclear Spin-Free Iron(III) Complex
Zadrozny, J. M., & Freedman, D. E. (2015) · Inorganic Chemistry · doi:10.1021/acs.inorgchem.5b02429 · Cited by 51 · see citing works
Quantum processor-inspired machine learning in the biomedical sciences
Li, R. Y., Gujja, S., Bajaj, S. R., Gamel, O., Cilfone, N. A., Gulcher, J. R., Lidar, D. A., & Chittenden, T. W. (2021) · Patterns · doi:10.1016/j.patter.2021.100246 · Cited by 49 · see citing works
Polymer physics by quantum computing
Micheletti, C., Hauke, P., & Faccioli, P. (2021) · Physical Review Letters · doi:10.1103/physrevlett.127.080501 · Cited by 48 · see citing works
Molecular docking via quantum approximate optimization algorithm
Ding, Q.-M., Huang, Y., & Yuan, X. (2024) · Physical Review Applied · doi:10.1103/physrevapplied.21.034036 · Cited by 48 · see citing works
Finding spin glass ground states using quantum walks
Callison, A., Chancellor, N., Mintert, F., & Kendon, V. (2019) · New Journal of Physics · doi:10.1088/1367-2630/ab5ca2 · Cited by 48 · see citing works
Quantum computing algorithms: getting closer to critical problems in computational biology
Marchetti, L., Nifosì, R., Martelli, P. L., Da Pozzo, E., Cappello, V., Banterle, F., Trincavelli, M. L., Martini, C., & D’Elia, M. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac437 · Cited by 47 · see citing works
Maximum-Entropy Inference with a Programmable Annealer
Chancellor, N., Szőke, S., Vinci, W., Aeppli, G., & Warburton, P. A. (2016) · Scientific Reports · doi:10.1038/srep22318 · Cited by 47 · see citing works
Coarse-grained lattice protein folding on a quantum annealerPreprint
Babej, T., Ing, C. E., & Fingerhuth, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.00713 · Cited by 47 · see citing works
Babbush, R., Perdomo‐Ortiz, A., O'Gorman, B., Macready, W., & Aspuru‐Guzik, A. (2014) · Advances in Chemical Physics · doi:10.1002/9781118755815.ch05 · Cited by 45 · see citing works
Self-citationDetermination and correction of persistent biases in quantum annealers
Perdomo‐Ortiz, A., O’Gorman, B., Fluegemann, J., Biswas, R., & Smelyanskiy, V. (2016) · Scientific Reports · doi:10.1038/srep18628 · Cited by 45 · see citing works
Self-citationResource efficient gadgets for compiling adiabatic quantum optimization problems
Babbush, R., O'Gorman, B., & Aspuru‐Guzik, A. (2013) · Annalen der Physik · doi:10.1002/andp.201300120 · Cited by 45 · see citing works
Self-citationQuantum search with hybrid adiabatic–quantum-walk algorithms and realistic noise
Morley, J. G., Chancellor, N., Bose, S., & Kendon, V. M. (2019) · Physical Review A · doi:10.1103/physreva.99.022339 · Cited by 45 · see citing works
Kanao, T., & Goto, H. (2021) · npj Quantum Information · doi:10.1038/s41534-020-00355-1 · Cited by 45 · see citing works
- Authors
- Perdomo-Ortiz, A., Dickson, N., Drew-Brook, M., Rose, G., & Aspuru-Guzik, A.
- Affiliations
- D-Wave Systems (Canada) (CA)
- Venue
- Scientific Reports
- Published
- 2012-08-13
- Volume
- 2
- Issue
- 1
- Pages
- 571-571
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC3417777
- OpenAlex
- W2118518847
- Cited by
- 366
- References
- 63
- Citations by year
- 2026: 20 · 2025: 23 · 2024: 36 · 2023: 35 · 2022: 42 · 2021: 44 · 2020: 27 · 2019: 33 · 2018: 13 · 2017: 11 · 2016: 22 · 2015: 18 · 2014: 25 · 2013: 12 · 2012: 5
- FWCI
- 18.90
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Scientific Reports (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1037992)
- Division of Chemistry (CHE-1037992)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › DNA and Biological Computing
- Keywords
- quantum annealing, hydrophobic-polar model, low-energy conformations, protein energy landscape, superconducting qubits, combinatorial optimization
- MeSH terms
- Algorithms, Amino Acid Sequence, Computer Simulation, Models, Molecular, Protein Conformation, Proteins, Quantum Theory, Protein Folding
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