Towards practical applications in quantum computational biology
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.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The best achievable quadratic speedup on the search problem has already been demonstrated with Grover's algorithm.
“An exception to it is the best achievable quadratic speedup on search problem, which has already been demonstrated with grover's algorithm [41].”
From Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics (2024) · cites this paper as [41]
A single-nucleotide sequence model's feature has been solved using QA devices.
“A single-nucleotide sequence model's feature has been solved using QA devices [40, 41].”
From Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics (2024) · cites this paper as [41]
Grover's algorithm determines one specific output out of N options in the case of queries.
“Grover's algorithm determines one specific output out of N options in the case of queries [41].”
From Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics (2024) · cites this paper as [41]
Other biomolecular systems have been proposed as possible beneficiaries of future QC developments, because their complex electronic structure is only partially addressable using classical computation schemes.
“Other biomolecular systems have been proposed as possible beneficiaries of future QC developments, because their complex electronic structure is only partially addressable using classical computation schemes [117].”
From Quantum computing algorithms: getting closer to critical problems in computational biology (2022) · cites this paper as [117]
Cites
Quantum Computing in the NISQ era and beyond
Preskill, J. (2018) · Quantum · doi:10.22331/q-2018-08-06-79 · Cited by 8,830 · see citing works
Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer
Shor, P. W. (1997) · SIAM Journal on Computing · doi:10.1137/s0097539795293172 · Cited by 8,472 · see citing works
Quantum supremacy using a programmable superconducting processor
Arute, F., Arya, K., Babbush, R., Bacon, D., Bardin, J. C., Barends, R., Biswas, R., Boixo, S., Brandao, F. G. S. L., Buell, D. A., et al. (2019) · Nature · doi:10.1038/s41586-019-1666-5 · Cited by 7,720 · see citing works
Simulating physics with computers
Feynman, R. P. (1982) · International Journal of Theoretical Physics · doi:10.1007/bf02650179 · Cited by 7,691 · see citing works
Cramming More Components onto Integrated Circuits (1965)
Moore, G. E. (2021) · The MIT Press eBooks · doi:10.7551/mitpress/12274.003.0027 · Cited by 6,274 · see citing works
Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., & Lloyd, S. (2017) · Nature · doi:10.1038/nature23474 · Cited by 4,779 · see citing works
Ladd, T. D., Jelezko, F., Laflamme, R., Nakamura, Y., Monroe, C., & O’Brien, J. L. (2010) · Nature · doi:10.1038/nature08812 · Cited by 3,592 · see citing works
Quantum Algorithm for Linear Systems of Equations
Harrow, A. W., Hassidim, A., & Lloyd, S. (2009) · Physical Review Letters · doi:10.1103/physrevlett.103.150502 · Cited by 3,325 · see citing works
Lloyd, S. (1996) · Science · doi:10.1126/science.273.5278.1073 · Cited by 3,088 · see citing works
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
Probing many-body dynamics on a 51-atom quantum simulator
Bernien, H., Schwartz, S., Keesling, A., Levine, H., Omran, A., Pichler, H., Choi, S., Zibrov, A. S., Endres, M., Greiner, M., et al. (2017) · Nature · doi:10.1038/nature24622 · Cited by 2,460 · see citing works
Proton-Coupled Electron Transfer
Weinberg, D. R., Gagliardi, C. J., Hull, J. F., Murphy, C. F., Kent, C. A., Westlake, B. C., Paul, A., Ess, D. H., McCafferty, D. G., & Meyer, T. J. (2012) · Chemical Reviews · doi:10.1021/cr200177j · Cited by 2,433 · see citing works
Quantum computational advantage using photons
Zhong, H.-S., Wang, H., Deng, Y.-H., Chen, M.-C., Peng, L.-C., Luo, Y.-H., Qin, J., Wu, D., Ding, X., Hu, Y., et al. (2020) · Science · doi:10.1126/science.abe8770 · Cited by 2,405 · see citing works
The chips are down for Moore’s law
Waldrop, M. M. (2016) · Nature · doi:10.1038/530144a · Cited by 2,397 · see citing works
Feynman, R. P. (1986) · Foundations of Physics · doi:10.1007/bf01886518 · Cited by 1,770 · see citing works
Protein Structure Prediction Using Rosetta
Rohl, C. A., Strauss, C. E. M., Misura, K. M. S., & Baker, D. A. (2004) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/s0076-6879(04)83004-0 · Cited by 1,711 · see citing works
The Protein-Folding Problem, 50 Years On
Dill, K. A., & MacCallum, J. L. (2012) · Science · doi:10.1126/science.1219021 · Cited by 1,699 · see citing works
Proton-coupled electron transfer
Huynh, M. H. V., & Meyer, T. J. (2007) · Chemical Reviews · doi:10.1021/cr0500030 · Cited by 1,659 · see citing works
Quantum 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
Quantum 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
Albash, T., & Lidar, D. A. (2018) · Reviews of Modern Physics · doi:10.1103/revmodphys.90.015002 · Cited by 1,546 · see citing works
Strengths and Weaknesses of Quantum Computing
Bennett, C. H., Bernstein, E., Brassard, G., & Vazirani, U. (1997) · SIAM Journal on Computing · doi:10.1137/s0097539796300933 · Cited by 1,470 · see citing works
Quantum Chemistry in the Age of Quantum Computing
Cao, Y., Romero, J., Olson, J., Degroote, M., Johnson, P., Kieferová, M., Kivlichan, I., Menke, T., Peropadre, B., Sawaya, N., et al. (n.d.) · OPUS - Open Publications of UTS Scholars (University of Technology Sydney) · Cited by 1,265 · see citing works
Theory for the folding and stability of globular proteins
Dill, K. A. (1985) · Biochemistry · doi:10.1021/bi00327a032 · Cited by 1,256 · see citing works
Observation of a many-body dynamical phase transition with a 53-qubit quantum simulator
Zhang, J., Pagano, G., Hess, P. W., Kyprianidis, A., Becker, P., Kaplan, H., Gorshkov, A. V., Gong, Z.-X., & Monroe, C. (2017) · Nature · doi:10.1038/nature24654 · Cited by 1,079 · see citing works
Quantum algorithms: an overview
Montanaro, A. (2016) · npj Quantum Information · doi:10.1038/npjqi.2015.23 · Cited by 1,071 · see citing works
Lambert, N., Chen, Y.-N., Cheng, Y.-C., Li, C.-M., Chen, G.-Y., & Nori, F. (2013) · Nature Physics · doi:10.1038/nphys2474 · Cited by 948 · see citing works
Ultrafast Dynamics of Carotenoid Excited States−From Solution to Natural and Artificial Systems
Polı́vka, T., & Sundström, V. (2004) · Chemical Reviews · doi:10.1021/cr020674n · Cited by 897 · see citing works
Moore, G. E. (2006) · IEEE Solid-State Circuits Society Newsletter · doi:10.1109/n-ssc.2006.4785860 · Cited by 854 · see citing works
Quantum Algorithms for Quantum Chemistry and Quantum Materials Science
Bauer, B., Bravyi, S., Motta, M., & Chan, G. K.-L. (2020) · Chemical Reviews · doi:10.1021/acs.chemrev.9b00829 · Cited by 848 · see citing works
Quantum computational supremacy
Harrow, A. W., & Montanaro, A. (2017) · Nature · doi:10.1038/nature23458 · Cited by 823 · 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
Dynamics of Light Harvesting in Photosynthesis
Cheng, Y., & Fleming, G. R. (2008) · Annual Review of Physical Chemistry · doi:10.1146/annurev.physchem.040808.090259 · Cited by 784 · see citing works
Elucidating reaction mechanisms on quantum computers
Reiher, M., Wiebe, N., Svore, K. M., Wecker, D., & Troyer, M. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1619152114 · Cited by 745 · see citing works
Proton Tunneling in DNA and its Biological Implications
Löwdin, P.-O. (1963) · Reviews of Modern Physics · doi:10.1103/revmodphys.35.724 · Cited by 656 · see citing works
Defining and detecting quantum speedup
Rønnow, T. F., Wang, Z., Job, J., Boixo, S., Isakov, S. V., Wecker, D., Martinis, J. M., Lidar, D. A., & Troyer, M. (2014) · Science · doi:10.1126/science.1252319 · Cited by 601 · see citing works
Self-verifying variational quantum simulation of lattice models
Kokail, C., Maier, C., van Bijnen, R., Brydges, T., Joshi, M. K., Jurcevic, P., Muschik, C. A., Silvi, P., Blatt, R., Roos, C. F., et al. (2019) · Nature · doi:10.1038/s41586-019-1177-4 · Cited by 594 · see citing works
Quantum approximate optimization of non-planar graph problems on a planar superconducting processor
Harrigan, M. P., Sung, K. J., Neeley, M., Satzinger, K. J., Arute, F., Arya, K., Atalaya, J., Bardin, J. C., Barends, R., Boixo, S., et al. (2021) · Nature Physics · doi:10.1038/s41567-020-01105-y · Cited by 564 · see citing works
Quantum computing with neutral atoms
Henriet, L., Beguin, L., Signoles, A., Lahaye, T., Browaeys, A., Reymond, G.-O., & Jurczak, C. (2020) · Quantum · doi:10.22331/q-2020-09-21-327 · Cited by 550 · see citing works
FAULT-TOLERANT QUANTUM COMPUTATION
Preskill, J. P. (1998) · WORLD SCIENTIFIC eBooks · doi:10.1142/9789812385253_0008 · Cited by 525 · see citing works
‘It will change everything’: DeepMind’s AI makes gigantic leap in solving protein structures
Callaway, E. (2020) · Nature · doi:10.1038/d41586-020-03348-4 · Cited by 499 · see citing works
Limits on fundamental limits to computation
Markov, I. L. (2014) · Nature · doi:10.1038/nature13570 · Cited by 494 · see citing works
Hydrogen Tunneling in Enzyme Reactions
Cha, Y., Murray, C. J., & Klinman, J. P. (1989) · Science · doi:10.1126/science.2646716 · Cited by 475 · see citing works
Structural Enzymology of Nitrogenase Enzymes
Einsle, O., & Rees, D. C. (2020) · Chemical Reviews · doi:10.1021/acs.chemrev.0c00067 · Cited by 420 · see citing works
Adiabatic Quantum Computation Is Equivalent to Standard Quantum Computation
Aharonov, D., van Dam, W., Kempe, J., Landau, Z., Lloyd, S., & Regev, O. (2008) · SIAM Review · doi:10.1137/080734479 · Cited by 395 · see citing works
Finding low-energy conformations of lattice protein models by quantum annealing
Perdomo-Ortiz, A., Dickson, N., Drew-Brook, M., Rose, G., & Aspuru-Guzik, A. (2012) · Scientific Reports · doi:10.1038/srep00571 · Cited by 366 · 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 · cited 2× · see citing works
“One of the intriguing next stages in the development of quantum algorithms for computational biology is related to improving computational approaches to computer-aided drug discovery 48 .”
BackgroundBoson sampling for molecular vibronic spectra
Huh, J., Guerreschi, G. G., Peropadre, B., McClean, J. R., & Aspuru-Guzik, A. (2015) · Nature Photonics · doi:10.1038/nphoton.2015.153 · Cited by 341 · see citing works
Even More Efficient Quantum Computations of Chemistry Through Tensor Hypercontraction
Lee, J., Berry, D. W., Gidney, C., Huggins, W. J., McClean, J. R., Wiebe, N., & Babbush, R. (2021) · PRX Quantum · doi:10.1103/prxquantum.2.030305 · Cited by 304 · see citing works
High-order quantum algorithm for solving linear differential equations
Berry, D. W. (2014) · Journal of Physics A: Mathematical and Theoretical · doi:10.1088/1751-8113/47/10/105301 · Cited by 277 · see citing works
Quantum computing enhanced computational catalysis
von Burg, V., Low, G. H., Häner, T., Steiger, D. S., Reiher, M., Roetteler, M., & Troyer, M. (2021) · Physical Review Research · doi:10.1103/physrevresearch.3.033055 · Cited by 259 · see citing works
Andruniów, T., Ferré, N., & Olivucci, M. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0407997101 · Cited by 239 · 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
Proton-Coupled Electron Transfer in Soybean Lipoxygenase
Hatcher, E., Soudackov, A. V., & Hammes‐Schiffer, S. (2004) · Journal of the American Chemical Society · doi:10.1021/ja039606o · Cited by 224 · see citing works
Qubitization of Arbitrary Basis Quantum Chemistry Leveraging Sparsity and Low Rank Factorization
Berry, D. W., Gidney, C., Motta, M., McClean, J. R., & Babbush, R. (2019) · Quantum · doi:10.22331/q-2019-12-02-208 · Cited by 222 · see citing works
Quantum 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
Li, R. Y., Di Felice, R., Rohs, R., & Lidar, D. A. (2018) · npj Quantum Information · doi:10.1038/s41534-018-0060-8 · Cited by 202 · see citing works
Quantum-Mechanical Modeling of the Femtosecond Isomerization in Rhodopsin
Hahn, S., & Stock, G. (2000) · The Journal of Physical Chemistry B · doi:10.1021/jp992939g · Cited by 171 · see citing works
Probing for quantum speedup in spin-glass problems with planted solutions
Hen, I., Job, J., Albash, T., Rønnow, T. F., Troyer, M., & Lidar, D. A. (2015) · Physical Review A · doi:10.1103/physreva.92.042325 · Cited by 165 · 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
Clique-detection models in computational biochemistry and genomics
Butenko, S. I., & Wilhelm, W. E. (2005) · European Journal of Operational Research · doi:10.1016/j.ejor.2005.05.026 · Cited by 162 · see citing works
High-precision quantum algorithms for partial differential equations
Childs, A. M., Liu, J.-P., & Ostrander, A. (2021) · Quantum · doi:10.22331/q-2021-11-10-574 · Cited by 157 · see citing works
Searching for quantum speedup in quasistatic quantum annealers
Amin, M. H. S. (2015) · Physical Review A · doi:10.1103/physreva.92.052323 · Cited by 154 · see citing works
Katzgraber, H. G., Hamze, F., & Andrist, R. S. (2014) · Physical Review X · doi:10.1103/physrevx.4.021008 · Cited by 145 · see citing works
Analogue quantum chemistry simulation
Argüello-Luengo, J., González-Tudela, A., Shi, T., Zoller, P., & Cirac, J. I. (2019) · Nature · doi:10.1038/s41586-019-1614-4 · Cited by 136 · see citing works
Quantum Spectral Methods for Differential Equations
Childs, A. M., & Liu, J.-P. (2020) · Communications in Mathematical Physics · doi:10.1007/s00220-020-03699-z · Cited by 133 · see citing works
Efficient quantum simulation of photosynthetic light harvesting
Wang, B.-X., Tao, M.-J., Ai, Q., Xin, T., Lambert, N., Ruan, D., Cheng, Y.-C., Nori, F., Deng, F.-G., & Long, G.-L. (2018) · npj Quantum Information · doi:10.1038/s41534-018-0102-2 · Cited by 124 · 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
Leveraging Secondary Storage to Simulate Deep 54-qubit Sycamore CircuitsPreprint
Pednault, E., Gunnels, J. A., Nannicini, G., Lior Horesh, C., & Wisnieff, R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.09534 · Cited by 119 · see citing works
Proton-Coupled Electron Transfer in a Model for Tyrosine Oxidation in Photosystem II
Carra, C., Iordanova, N., & Hammes‐Schiffer, S. (2003) · Journal of the American Chemical Society · doi:10.1021/ja035588z · Cited by 101 · see citing works
Neugebauer, J. (2008) · The Journal of Physical Chemistry B · doi:10.1021/jp709956k · Cited by 94 · see citing works
Measuring the similarity of graphs with a Gaussian boson sampler
Schuld, M., Brádler, K., Israel, R., Su, D., & Gupt, B. (2020) · Physical Review A · doi:10.1103/physreva.101.032314 · Cited by 94 · 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
How "Quantum" is the D-Wave Machine?
Shin, S. W., Smith, G., Smolin, J. A., & Vazirani, U. (2014) · arXiv · doi:10.48550/arxiv.1401.7087 · Cited by 89 · see citing works
Classical Simulation of Quantum Supremacy CircuitsPreprint
Huang, C., Zhang, F., Newman, M., Cai, J., Gao, X., Tian, Z., Wu, J., Xu, H., Yu, H., Yuan, B., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.06787 · Cited by 76 · see citing works
Ontologies and Standards in Bioscience Research: For Machine or for Human
Mi, H., & Thomas, P. D. (2011) · Frontiers in Physiology · doi:10.3389/fphys.2011.00005 · Cited by 75 · see citing works
First-principles calculation of electronic spectra of light-harvesting complex II
König, C., & Neugebauer, J. (2011) · Physical Chemistry Chemical Physics · doi:10.1039/c0cp02808h · Cited by 71 · see citing works
A quantum alternating operator ansatz with hard and soft constraints for lattice protein foldingPreprint
Fingerhuth, M., Babej, T., & Ing, C. E. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.13411 · Cited by 70 · see citing works
Designing Peptides on a Quantum Computer
Mulligan, V. K., Melo, H., Merritt, H. I., Slocum, S., Weitzner, B. D., Watkins, A. M., Renfrew, P. D., Pelissier, C., Arora, P. S., & Bonneau, R. (2019) · doi:10.1101/752485 · Cited by 67 · see citing works
A quantum algorithm to solve nonlinear differential equations
Leyton, S. K., & Osborne, T. J. (2008) · arXiv · doi:10.48550/arxiv.0812.4423 · Cited by 65 · 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
Self-citationQuASeR: Quantum Accelerated de novo DNA sequence reconstruction
Sarkar, A., Al-Ars, Z., & Bertels, K. (2021) · PLoS ONE · doi:10.1371/journal.pone.0249850 · Cited by 61 · see citing works
Boundaries of quantum supremacy via random circuit sampling
Zlokapa, A., Villalonga, B., Boixo, S., & Lidar, D. A. (2023) · npj Quantum Information · doi:10.1038/s41534-023-00703-x · Cited by 54 · see citing works
Fast quantum search algorithms in protein sequence comparisons: Quantum bioinformatics
Hollenberg, L. C. L. (2000) · Physical Review E · doi:10.1103/physreve.62.7532 · Cited by 33 · see citing works
Α Quantum Pattern Recognition Method for Improving Pairwise Sequence Alignment
Prousalis, K., & Konofaos, N. (2019) · Scientific Reports · doi:10.1038/s41598-019-43697-3 · Cited by 33 · see citing works
Serret, M. F., Marchand, B., & Ayral, T. (2020) · Physical Review A · doi:10.1103/physreva.102.052617 · Cited by 32 · see citing works
Quantum Sampling Algorithms for Near-Term Devices
Wild, D. S., Sels, D., Pichler, H., Zanoci, C., & Lukin, M. D. (2021) · Physical Review Letters · doi:10.1103/physrevlett.127.100504 · Cited by 30 · see citing works
Filling the Gap With Sand: When CMOS Reaches THz
Naghavi, S. M. H., Taba, M. T., Han, R., Aseeri, M. A., Cathelin, A., & Afshari, E. (2019) · IEEE Solid-State Circuits Magazine · doi:10.1109/mssc.2019.2922888 · Cited by 25 · see citing works
QiBAM: approximate sub-string index search on quantum accelerators applied to DNA read alignment
Sarkar, A., Al-Ars, Z., Almudever, C. G., & Bertels, K. L. M. (2021) · Electronics · doi:10.3390/electronics10192433 · Cited by 20 · see citing works
A Variable Neighbourhood Descent Heuristic for Conformational Search Using a Quantum Annealer
Marchand, D. J. J., Noori, M., Roberts, A., Rosenberg, G., Woods, B., Yildiz, U., Coons, M. P., Devore, D. D., & Margl, P. M. (2019) · Scientific Reports · doi:10.1038/s41598-019-47298-y · Cited by 16 · see citing works
An algorithm for DNA read alignment on quantum acceleratorsPreprint
Sarkar, A., Al-Ars, Z., Almudéver, C. G., & Bertels, K. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.05563 · Cited by 15 · see citing works
Mittal, V. K., & McDonald, J. F. (2017) · BMC Medical Genomics · doi:10.1186/s12920-017-0289-7 · Cited by 14 · see citing works
Quantum speedups of some general-purpose numerical optimisation algorithms
Montanaro, A., Linden, N., Alexandru, C.-M., Bridgett-Tomkinson, E., MacManus, J. P., & Morris, H. (2020) · Bristol Research (University of Bristol) · doi:10.1088/2058-9565/abb003 · Cited by 14 · see citing works
Shallow Distance Dependence for Proton-Coupled Tyrosine Oxidation in Oligoproline Peptides
Koronkiewicz, B., Swierk, J. R., Regan, K. P., & Mayer, J. M. (2020) · Journal of the American Chemical Society · doi:10.1021/jacs.0c01429 · Cited by 13 · see citing works
Quantum Methods for Sequence Alignment and Metagenomics
Lindvall, O. B. (2019) · Cited by 5 · see citing works
A Variable Neighbourhood Descent Heuristic for Conformational Search Using a Quantum AnnealerPreprint
Marchand, D. J. J., Noori, M., Roberts, A., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.06999 · Cited by 1 · see citing works
Flagged first, then by citations
3 of the 99 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
The state of quantum computing applications in health and medicine
Flöther, F. F. (2023) · Research Directions Quantum Technologies · doi:10.1017/qut.2023.4 · Cited by 128 · see citing works
Quantum Computing for Molecular Biology**
Baiardi, A., Christandl, M., & Reiher, M. (2023) · ChemBioChem · doi:10.1002/cbic.202300120 · Cited by 93 · 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
Recent Advances in Quantum Computing for Drug Discovery and Development
Kumar, G., Yadav, S., Mukherjee, A., Hassija, V., & Guizani, M. (2024) · IEEE Access · doi:10.1109/access.2024.3376408 · Cited by 93 · 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
“For H0, the ground state is known a situation governed by H1 An exception to it is the best achievable quadratic speedup on search problem, which has already been demonstrated with grover’s algorithm [41].”
InfluentialGenome 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
Self-citationImplementation 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
Hybrid quantum-classical machine learning for generative chemistry and drug design
Gircha, A. I., Boev, A. S., Avchaciov, K., Fedichev, P. O., & Fedorov, A. K. (2023) · Scientific Reports · doi:10.1038/s41598-023-32703-4 · Cited by 57 · see citing works
Self-citationSingh, H., Majumder, S., & Mishra, S. (2023) · The Journal of Chemical Physics · doi:10.1063/5.0161057 · Cited by 51 · 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
“Other biomolecular systems have been proposed as possible beneficiaries of future QC developments, because their complex electronic structure is only partially addressable using classical computation schemes [117].”
BackgroundModeling and designing a robotic swarm: A quantum computing approach
Mannone, M., Seidita, V., & Chella, A. (2023) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2023.101297 · Cited by 45 · see citing works
A primer for quantum computing and its applications to healthcare and biomedical research
Durant, T. J. S., Knight, E., Nelson, B. G., Dudgeon, S. N., Lee, S. J., Walliman, D., Young, H. P., Lucila Ohno-machado, A., & Schulz, W. L. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae149 · Cited by 41 · see citing works
Quantum computing research in medical sciences
Alrashed, S., & Min‐Allah, N. (2024) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2024.101606 · Cited by 40 · see citing works
Variational quantum eigensolver techniques for simulating carbon monoxide oxidation
Sapova, M. D., & Fedorov, A. K. (2022) · Communications Physics · doi:10.1038/s42005-022-00982-4 · Cited by 32 · see citing works
Self-citationQuantum planning for swarm robotics
Chella, A., Gaglio, S., Mannone, M., Pilato, G., Seidita, V., Vella, F., & Zammuto, S. (2023) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2023.104362 · Cited by 30 · see citing works
Quantum algorithms for scientific computing
Au-Yeung, R., Camino, B., Rathore, O., & Kendon, V. M. (2024) · Reports on Progress in Physics · doi:10.1088/1361-6633/ad85f0 · Cited by 25 · see citing works
How can quantum technologies be applied in healthcare, medicine and the life sciences?
Flöther, F. F., & Griffin, P. F. (2023) · Research Directions Quantum Technologies · doi:10.1017/qut.2023.1 · Cited by 17 · see citing works
The role of entanglement for enhancing the efficiency of quantum kernels towards classification
Sharma, D., Singh, P., & Kumar, A. (2023) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2023.128938 · Cited by 17 · see citing works
Advancing biomolecular simulation through exascale HPC, AI and quantum computing
Pyzer‐Knapp, E. O., & Curioni, A. (2024) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102826 · Cited by 16 · see citing works
Belaloui, N. E., Tounsi, A., Khamadja, A. R., Louamri, M. M., Benslama, A., Bernal, D. E., & Rouabah, M. T. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c01657 · Cited by 13 · see citing works
Haplotype-resolved assembly of diploid and polyploid genomes using quantum computing
Chen, Y., Huang, J.-H., Sun, Y., Zhang, Y., Li, Y., & Xu, X. (2024) · Cell Reports Methods · doi:10.1016/j.crmeth.2024.100754 · Cited by 10 · see citing works
Bioinformatics in Russia: history and present-day landscape
Nawaz, M. A., Pamirsky, I. E., & Golokhvast, K. S. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae513 · Cited by 9 · see citing works
Misaghian, K., Lugo, J. E., & Faubert, J. (2024) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2024.1348712 · Cited by 8 · see citing works
Potential of quantum computing to effectively comprehend the complexity of brain
Sihare, S. R. (2023) · Applied Intelligence · doi:10.1007/s10489-023-04857-1 · Cited by 8 · see citing works
Early quantum computing applications on the path towards precision medicine
Flöther, F. F. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-98808-7.00001-1 · Cited by 7 · see citing works
Kamran, M., Zhijun, T., Ullah, A., Alvi, M. H. A., Aqeel, T., Xuejun, C., Sagheer, S., Umaira, Bingguang, X., & Xu, H. (2026) · Plant Stress · doi:10.1016/j.stress.2026.101317 · Cited by 6 · see citing works
Exploring ground states of Fermi-Hubbard model on honeycomb lattices with counterdiabaticity
Tang, J., Xu, R., Ding, Y., Xu, X., Ban, Y., Yung, M., Pérez-Obiol, A., Platero, G., & Chen, X. (2024) · npj Quantum Materials · doi:10.1038/s41535-024-00697-5 · Cited by 5 · see citing works
Quantum computing applications in biology
Ghamsari, M. S. (2025) · Discover Computing · doi:10.1007/s10791-025-09803-y · Cited by 4 · see citing works
The Sound of Swarm. Auditory Description of Swarm Robotic Movements
Mannone, M., Seidita, V., & Chella, A. (2023) · ACM Transactions on Human-Robot Interaction · doi:10.1145/3596203 · Cited by 4 · see citing works
A Bird's Eye View on Quantum Computing: Current and Future Trends
Branchini, B., Conficconi, D., Peverelli, F., Sciuto, D., & Santambrogio, M. D. (2023) · IEEE EUROCON - International Conference on Computer as a Tool (EUROCON) · doi:10.1109/eurocon56442.2023.10198957 · Cited by 4 · see citing works
Quantum-like Data Modeling in Applied Sciences: Review
Lipovetsky, S. (2023) · Stats · doi:10.3390/stats6010021 · Cited by 4 · see citing works
Surrogate optimization of variational quantum circuits
Gustafson, E., Tiihonen, J., Chamaki, D., Sorourifar, F., Mullinax, J. W., Li, A. C. Y., Maciejewski, F. B., Sawaya, N. P. D., Krogel, J. T., Bernal, D. E., et al. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2408530122 · Cited by 3 · see citing works
岳, 天., 吴, 晨., 刘, 熠., 杜, 正., 赵, 娜., 焦, 毅., 徐, 喆., & 史, 文. (2023) · SCIENTIA SINICA Terrae · doi:10.1360/sste-2022-0319 · Cited by 3 · see citing works
A QUBO formulation for the Tree Containment problem
Dinneen, M. J., Ghodla, P. S., & Linz, S. (2022) · Theoretical Computer Science · doi:10.1016/j.tcs.2022.09.012 · Cited by 3 · see citing works
Fostering multidisciplinary collaborations
(2021) · Nature Computational Science · doi:10.1038/s43588-021-00123-x · Cited by 3 · see citing works
Yao, Q., & Li, H. (2025) · New Journal of Physics · doi:10.1088/1367-2630/ae0360 · Cited by 2 · see citing works
Ugya, A. Y., & Meguellati, K. (2022) · Egyptian Journal of Basic and Applied Sciences · doi:10.1080/2314808x.2022.2082133 · Cited by 2 · see citing works
Quantum technologies in healthcare: analysis of cases and perspectives
Kobyakova, О. S., Yeremchenko, O., Kanev, A. A., & Kurakova, N. G. (2025) · Vrach i informacionnye tehnologii · doi:10.25881/18110193_2025_1_6 · Cited by 2 · see citing works
Makushin, K. M., & Fedorov, A. K. (2025) · Quantum Reports · doi:10.3390/quantum7020021 · Cited by 2 · see citing works
Self-citationChen, Y., Huang, J.-H., Sun, Y., Zhang, Y., Li, Y., & Xu, X. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4601824 · Cited by 2 · see citing works
A Brief Review on Quantum Drug Design
Ray, A., Das, P., Bhattacharyya, S., Platoš, J., Mršić, L., & Hassanien, A. E. (2024) · Smart innovation, systems and technologies · doi:10.1007/978-981-97-3980-6_41 · Cited by 1 · see citing works
Handling of Family Genetic and Genomic Data: An International Legal Aspect
Kubyshkin, A. V. (2024) · Lex Russica · doi:10.17803/1729-5920.2024.217.12.084-093 · Cited by 1 · see citing works
qblaze: An Efficient and Scalable Sparse Quantum Simulator
Venev, H., Udomsrirungruang, T., Dimitrov, D., Gehr, T., & Vechev, M. (2025) · Proceedings of the ACM on Programming Languages · doi:10.1145/3763066 · Cited by 1 · see citing works
Inference of Maximum Parsimony Phylogenetic Trees with Model‐Based Classical and Quantum Methods
Zhang, J., Chen, Y., Zhou, Y., & Huang, J.-H. (2026) · Advanced Quantum Technologies · doi:10.1002/qute.202500695 · Cited by 1 · see citing works
Belaloui, N. E., Tounsi, A., Khamadja, A. R., Benkadour, H., Louamri, M. M., Benslama, A., & Rouabah, M. T. (2025) · Physica Scripta · doi:10.1088/1402-4896/ae2f3e · Cited by 1 · see citing works
Quantum Computing in Healthcare: A New Frontier
Gandhi, K., Garg, D., Shah, M., Goel, P., & Gite, S. (2026) · Smart sensors, measurement and instrumentation · doi:10.1007/978-3-032-05507-1_26 · Cited by 1 · see citing works
Addressing ecological challenges from a quantum computing perspective
Clenet, M., Dion, M., & Blanchet, F. G. (2026) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.70236 · Cited by 2 · see citing works
Noise Robustness of a Multiparty Quantum Summation Protocol
Rodríguez-Otero, A., Neumann, N. M. P., van der Schoot, W., & Wezeman, R. S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-63778-0_26 · Cited by 1 · see citing works
Sathish, K., Jacob, R., Anbarasu, M., Gondi, D. S., Howard, E. M., & Haridas, D. (2025) · International Conference on Multidisciplinary Research and Innovations in Engineering (MRIE) · doi:10.1109/mrie66930.2025.11156577 · Cited by 1 · see citing works
RetractedMachine learning in bosonic quantum systems
Kan, S., & Mao, Y. (2025) · Elsevier eBooks · doi:10.1016/b978-0-44-330259-6.00017-7 · Cited by 1 · see citing works
Improving the performance of fermionic neural networks with the Slater exponential Ansatz
Bokhan, D., Kolchenko, M. M., Boev, A. S., Fedorov, A. K., & Trubnikov, D. N. (2023) · International Journal of Quantum Chemistry · doi:10.1002/qua.27107
Self-citationChen, Y., Huang, J.-H., Sun, Y., Zhang, Y., Li, Y., & Xu, X. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.19.563028
Genetic Engineering with Quantum Circuits: creating codes and studying BioBloQu genetic elementsPreprint
Pascoal, P. V., Bambil, D. R., Lima, R. N., de Oliveira, M. A., de Tacca, L. A., Vainstein, M. H., & Rech, E. L. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.02.651535
A Haskell Adiabatic DSL: Solving Classical Optimization Problems on Quantum Hardware
Li, L., Young, D., Graves, J. B., Dissanayake, C., & Sabry, A. (2025) · Proceedings of the ACM on Programming Languages · doi:10.1145/3747521
Technologies Related to Quantum Computing and Its Uses: An Overview
Gupta, D. N. (2025) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-23099
Hassan, W., Tabassum, M., & Nawaz, M. A. (2025) · Information systems engineering and management · doi:10.1007/978-3-031-99786-0_8
Дмитриевич, Ч. Д., Maksimov, E. G., Sidorenko, S. V., & Pishchalnikov, R. Y. (2025) · Russian Journal of Plant Physiology · doi:10.1134/s1021443725603659
Rahman, E., Michon, A., Rao, P., Ahmed, M., Joseph, J. H., Wu, W. T. L., CARRUTHERS, J. D. A., & Webb, W. R. (2025) · Toxicon · doi:10.1016/j.toxicon.2025.108950
A universal theory of high-accuracy surface modeling and its quantum intelligent computing methods
Yue, T., Wu, C., Zhao, N., Liu, Y., Wang, Y., Du, Z., Shi, W., & Fan, Z. (2026) · Chinese Science Bulletin (Chinese Version) · doi:10.1360/csb-2025-5716
Introduction to In Silico Modeling and Simulation
Joshi, D. M., Bhatt, H., & Patel, H. K. (2026) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9798898813635126010005
The Quantum–Bio-Singularity: Synthesis of Current Paradigms and a Roadmap for Therapeutic Frontiers
Roosan, D. (2026) · Springer eBooks · doi:10.1007/978-3-032-19536-4_15
Small Molecule Drug Discovery Enhanced by Quantum AI
Karim, M., Sojib, N., Khan, M. Z. I., & Roosan, D. (2026) · Springer eBooks · doi:10.1007/978-3-032-19536-4_8
- Authors
- Fedorov, A. K., & Gelfand, M. S.
- Affiliations
- Moscow Institute of Physics and Technology (RU)
- Skolkovo Institute of Science and Technology (RU)
- Corresponding author
- Fedorov, A. K.
- Venue
- Nature Computational Science
- Published
- 2021-02-22
- Volume
- 1
- Issue
- 2
- Pages
- 114-119
- ISSN
- 2662-8457 (ISSN-L)
- OpenAlex
- W3133092723
- Semantic Scholar
- 62bfe09e46c72bde94974010d259dcb4c43d0292
- Cited by
- 62
- References
- 99
- Citations by year
- 2026: 8 · 2025: 16 · 2024: 13 · 2023: 17 · 2022: 6 · 2021: 2
- FWCI
- 0.68
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature Computational Science (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › DNA and Biological Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Information and Cryptography
- Keywords
- quantum computing, quantum computing applications, bioinformatics, quantum advantage
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
