The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
Quantum computing at the frontiers of biological sciences
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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 cited source says what the claim attributes to it.
In the citing paper
“However, a tentative possibility for a quantum hardware solution is qRAM. It might provide the ability to coherently enquire a big dataset as a superposition of qubits in a quantum computer.”
From Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics (2024) · cites this paper as [8]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Recently, a startup company from China known as SpinQ Technology informed about the development of the first-desktop quantum computer with a high-speed processor [8, 19, 38, 39].”
From Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics (2024) · cites this paper as [8]
Experimental — accuracy not yet measuredIn the citing paper
“Interestingly, in recent years the possibility of using quantum instead of or in combination with classical computing to solve biocomputing problems has emerged [46, 47].”
From Quantum computing algorithms: getting closer to critical problems in computational biology (2022) · cites this paper as [46]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
Fast and accurate long-read alignment with Burrows-Wheeler transform.
Li, H., & Durbin, R. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp698 · Cited by 12,063
Fischl, B. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.01.021 · Cited by 9,526
A fast quantum mechanical algorithm for database search
Grover, L. K. (1996) · Proceedings of the twenty-eighth annual ACM symposium on Theory of computing - STOC '96 · doi:10.1145/237814.237866 · Cited by 8,830
Quantum Computation and Quantum Information: 10th Anniversary Edition
Nielsen, M. A., & Chuang, I. L. (2012) · doi:10.1017/cbo9780511976667 · Cited by 8,436
GCTA: a tool for genome-wide complex trait analysis
Yang, J., Lee, S. H., Goddard, M. E., & Visscher, P. M. (2011) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2010.11.011 · Cited by 8,279
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
A General Framework for Weighted Gene Co-Expression Network Analysis
Zhang, B., & Horvath, S. (2005) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1128 · Cited by 5,684
Howie, B. N., Donnelly, P., & Marchini, J. (2009) · PLoS Genetics · doi:10.1371/journal.pgen.1000529 · Cited by 3,928
What we can do and what we cannot do with fMRI
Logothetis, N. K. (2008) · Nature · doi:10.1038/nature06976 · Cited by 3,612
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
Self-citationHardware-efficient variational quantum eigensolver for small molecules and quantum magnets
Kandala, A., Mezzacapo, A., Temme, K., Takita, M., Brink, M., Chow, J. M., & Gambetta, J. M. (2017) · Nature · doi:10.1038/nature23879 · Cited by 2,982
Inference of human population history from individual whole-genome sequences
Li, H., & Durbin, R. (2011) · Nature · doi:10.1038/nature10231 · Cited by 2,590
Supervised learning with quantum-enhanced feature spaces
Havlíček, V., Córcoles, A. D., Temme, K., Harrow, A. W., Kandala, A., Chow, J. M., & Gambetta, J. M. (2019) · Nature · doi:10.1038/s41586-019-0980-2 · Cited by 2,572
Self-citationQuantum annealing in the transverse Ising model
Kadowaki, T., & Nishimori, H. (1998) · Physical Review E · doi:10.1103/physreve.58.5355 · Cited by 2,339
Quantum Machine Learning in Feature Hilbert Spaces
Schuld, M., & Killoran, N. (2019) · Physical Review Letters · doi:10.1103/physrevlett.122.040504 · Cited by 1,840
A gene-based association method for mapping traits using reference transcriptome data
Gamazon, E. R., Wheeler, H. E., Shah, K. P., Mozaffari, S. V., Aquino-Michaels, K., Carroll, R. J., Eyler, A. E., Denny, J. C., Nicolae, D. L., Cox, N. J., et al. (2015) · Nature Genetics · doi:10.1038/ng.3367 · Cited by 1,790
A Quantum Approximate Optimization Algorithm
Farhi, E., Goldstone, J., & Gutmann, S. (2014) · arXiv · doi:10.48550/arxiv.1411.4028 · Cited by 1,789
Quantum amplitude amplification and estimation
Brassard, G., Høyer, P., Mosca, M., & Tapp, A. (2002) · Contemporary Mathematics · doi:10.1090/conm/305/05215 · Cited by 1,680
Circuit-centric quantum classifiers
Schuld, M., Bocharov, A., Svore, K. M., & Wiebe, N. (2020) · Physical Review A · doi:10.1103/physreva.101.032308 · Cited by 1,057
Comprehensive functional genomic resource and integrative model for the human brain
Wang, D., Liu, S., Warrell, J., Won, H., Shi, X., Navarro, F. C. P., Clarke, D., Gu, M., Emani, P., Yang, Y. T., et al. (2018) · Science · doi:10.1126/science.aat8464 · Cited by 965
Self-citationLambert, 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
Giovannetti, V., Lloyd, S., & Maccone, L. (2008) · Physical Review Letters · doi:10.1103/physrevlett.100.160501 · Cited by 935
A survey of sequence alignment algorithms for next-generation sequencing
Li, H., & Homer, N. (2010) · Briefings in Bioinformatics · doi:10.1093/bib/bbq015 · Cited by 875
Importance of quantum decoherence in brain processes
Tegmark, M. (2000) · Physical Review E · doi:10.1103/physreve.61.4194 · Cited by 776
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 · cited 1×
“For quantum computers, heuristic algorithms include adiabatic optimization 16 and the quantum approximate optimization algorithm (QAOA) 17 .”
MethodQuantum Algorithm for Data Fitting
Wiebe, N., Braun, D., & Lloyd, S. (2012) · Physical Review Letters · doi:10.1103/physrevlett.109.050505 · Cited by 528
How Local Excitation-Inhibition Ratio Impacts the Whole Brain Dynamics
Deco, G., Ponce‐Alvarez, A., Hagmann, P., Romani, G. L., Mantini, D., & Corbetta, M. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.5068-13.2014 · Cited by 393
Counting unlabeled acyclic digraphs
Robinson, R. W. (1977) · Lecture notes in mathematics · doi:10.1007/bfb0069178 · Cited by 372
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
Self-citationA Quantum Algorithm for Finding the Minimum
Durr, C., & Hoyer, P. (1996) · arXiv · doi:10.48550/arxiv.quant-ph/9607014 · Cited by 359
Hierarchical Heterogeneity across Human Cortex Shapes Large-Scale Neural Dynamics
Demirtaş, M., Burt, J. B., Helmer, M., Ji, J. L., Adkinson, B. D., Glasser, M. F., Essen, D. C. V., Sotiropoulos, S. N., Anticevic, A., & Murray, J. D. (2019) · Neuron · doi:10.1016/j.neuron.2019.01.017 · Cited by 343
Self-citationFoss‐Feig, J. H., Adkinson, B. D., Ji, J. L., Yang, G., Srihari, V. H., McPartland, J. C., Krystal, J. H., Murray, J. D., & Anticevic, A. (2017) · Biological Psychiatry · doi:10.1016/j.biopsych.2017.03.005 · Cited by 328
Self-citationAn Ancestral Recombination Graph
Griffiths, R., & Marjoram, P. (1997) · The IMA volumes in mathematics and its applications · doi:10.1007/978-1-4757-2609-1_16 · Cited by 292
Implementing a distance-based classifier with a quantum interference circuit
Schuld, M., Fingerhuth, M., & Petruccione, F. (2017) · EPL (Europhysics Letters) · doi:10.1209/0295-5075/119/60002 · Cited by 285
Computer Science and Telecommunications Board, Intelligence Community Studies Board, Sciences, D. O. E. A. P., & National Academies of Sciences, E. (2018) · National Academies Press eBooks · doi:10.17226/25196 · Cited by 276
Commercialize quantum technologies in five years
Mohseni, M., Read, P., Neven, H., Boixo, S., Denchev, V., Babbush, R., Fowler, A., Smelyanskiy, V., & Martinis, J. (2017) · Nature · doi:10.1038/543171a · Cited by 268
McConnell, M. J., Moran, J. V., Abyzov, A., Akbarian, S., Bae, T., Cortes-Ciriano, I., Erwin, J. A., Fasching, L., Flasch, D. A., Freed, D., et al. (2017) · Science · doi:10.1126/science.aal1641 · Cited by 262
Self-citationQuantum Computer Systems for Scientific Discovery
Alexeev, Y., Bacon, D., Brown, K. R., Calderbank, R., Carr, L. D., Chong, F. T., DeMarco, B., Englund, D., Farhi, E., Fefferman, B., et al. (2021) · DSpace@MIT (Massachusetts Institute of Technology) · doi:10.6082/h7fqg-6vt65 · Cited by 257
Haplotype Reference Consortium, Evans, L. M., Tahmasbi, R., Vrieze, S. I., Abecasis, G. R., Das, S., Gazal, S., Bjelland, D. W., de Candia, T. R., Goddard, M. E., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0108-x · Cited by 239
Mejias, J. F., Murray, J. D., Kennedy, H., & Wang, X.-J. (2016) · Science Advances · doi:10.1126/sciadv.1601335 · Cited by 227
Self-citationBerry, D. W., Childs, A. M., Ostrander, A., & Wang, G. (2017) · Communications in Mathematical Physics · doi:10.1007/s00220-017-3002-y · Cited by 218
Quantum variational autoencoder
Khoshaman, A., Vinci, W., Denis, B., Andriyash, E., Sadeghi, H., & Amin, M. H. (2018) · Quantum Science and Technology · doi:10.1088/2058-9565/aada1f · Cited by 218
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
Limit on the Speed of Quantum Computation in Determining Parity
Farhi, E., Goldstone, J., Gutmann, S., & Sipser, M. (1998) · Physical Review Letters · doi:10.1103/physrevlett.81.5442 · Cited by 175
Guest Column: A Survey of Quantum Learning Theory
Arunachalam, S., & de Wolf, R. (2017) · ACM SIGACT News · doi:10.1145/3106700.3106710 · Cited by 152
Nonnegative/Binary matrix factorization with a D-Wave quantum annealer
O’Malley, D., Vesselinov, V. V., Alexandrov, B. S., & Alexandrov, L. B. (2018) · PLOS ONE · doi:10.1371/journal.pone.0206653 · Cited by 140
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
How anatomy shapes dynamics: a semi-analytical study of the brain at rest by a simple spin model
Deco, G., Senden, M., & Jirsa, V. (2012) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2012.00068 · Cited by 132
Li, Z., Li, J., Dattani, N. S., Umrigar, C. J., & Chan, G. K. (2019) · The Journal of Chemical Physics · doi:10.1063/1.5063376 · Cited by 121
String matching in O(n+m) quantum time
Ramesh, H., & Vinay, V (2003) · Journal of Discrete Algorithms · doi:10.1016/s1570-8667(03)00010-8 · Cited by 95
Quantum Pattern Matching Fast on Average
Montanaro, A. (2017) · Algorithmica · doi:10.1007/s00453-015-0060-4 · Cited by 84
Reinen, J., Chén, O. Y., Hutchison, R. M., Yeo, B. T. T., Anderson, K. M., Sabuncu, M. R., Öngür, D., Roffman, J. L., Smoller, J. W., Baker, J. T., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03462-y · Cited by 84
Self-citationA quantum algorithm to solve nonlinear differential equations
Leyton, S. K., & Osborne, T. J. (2008) · arXiv · doi:10.48550/arxiv.0812.4423 · Cited by 65
The U.S. National Quantum Initiative: From Act to action
Monroe, C. R., Raymer, M. G., & Taylor, J. M. (2019) · Science · doi:10.1126/science.aax0578 · Cited by 53
Self-citationSmall quantum computers and large classical data setsPreprint
Harrow, A. W. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.00026 · Cited by 52
Self-citationHidden Quantum Markov Models and non-adaptive read-out of many-body statesPreprint
Monràs, A., Beige, A., & Wiesner, K. (2010) · Bristol Research (University of Bristol) · doi:10.48550/arxiv.1002.2337 · Cited by 42
Towards quantum simulations of biological information flow
Dorner, R., Goold, J., & Vedral, V. (2012) · Interface Focus · doi:10.1098/rsfs.2011.0109 · Cited by 27
Focus on quantum science and technology initiatives around the world
Thew, R., Jennewein, T., & Sasaki, M. (2020) · Quantum Science and Technology · doi:10.1088/2058-9565/ab5992 · Cited by 27
Kathuria, K., Ratan, A., McConnell, M. J., & Bekiranov, S. (2020) · Quantum Machine Intelligence · doi:10.1007/s42484-020-00017-7 · Cited by 25
Self-citation
Research works printed but not linked: 10
- Li, Z., Li, J., Dattani, N. S., Umrigar, C. J. & Chan, G. K. L. J. Chem. Phys. 150, 024302 (2019).
- Ambainis, A. & Kokainis, M. in Proc. 49th Annual ACM SIGACT Symposium on Theory of Computing – STOC 2017 989–1002 (2017).
- Kadowaki, T. & Nishimori, H. Phys. Rev. E Stat. Phys. Plasmas Fluids Relat. Interdiscip. Topics 58, 5355–5363 (1998).
- Srinivasan, S., Downey, C. & Boots, B. in Advances in Neural Information Processing Systems 31 (eds. Bengio, S. et al.) 10338–10347 (Curran Associates, 2018).
- Amin, M. H., Andriyash, E., Rolfe, J., Kulchytskyy, B. & Melko, R. Phys. Rev. X 8, 021050 (2018).
- Dezfouli, A., Morris, R., Ramos, F., Dayan, P. & Balleine, B. W. in Adv. Neural Information Processing Systems 31 (eds. Bengio, S. et al.) 4228–4237 (Curran Associates, 2018).
- Farhi, E. & Neven, H. Classification with quantum neural networks on near term processors. Preprint at https://arxiv.org/abs/1802.06002 (2018). · arXiv
- Tegmark, M. Phys. Rev. E Stat. Phys. Plasmas Fluids Relat. Interdiscip. Topics 61, 4194–4206 (2000).
- Marr, D. C. & Poggio, T. Neurosci. Res. Program Bull. 15, 470–488 (1977).
- Van Meter, R., Itoh, K. M. & Ladd, T. D. in MS+S 2006 – Controllable Quantum States: Mesoscopic Superconductivity and Spintronics, Proceedings of the International Symposium 183–188 (World Scientific Publishing, 2008).
Cited by
Sorted by citations
IBM quantum computers: evolution, performance, and future directions
AbuGhanem, M. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07047-7 · Cited by 266
Concepts and applications of digital twins in healthcare and medicine
Zhang, K., Zhou, H.-Y., Baptista‐Hon, D. T., Gao, Y., Liu, X., Oermann, E. K., Xu, S. H., Jin, S., Zhang, J., Sun, Z., et al. (2024) · Patterns · doi:10.1016/j.patter.2024.101028 · Cited by 185
Noisy intermediate-scale quantum computers
Cheng, B., Deng, X., Gu, X. Q., He, Y., Hu, G., Huang, P., Li, J., Lin, B., Lu, D., Lu, Y., et al. (2023) · Frontiers of Physics · doi:10.1007/s11467-022-1249-z · Cited by 147
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
An improved sparrow search algorithm based on quantum computations and multi-strategy enhancement
Wu, R., Huang, H., Wei, J., Ma, C., Zhu, Y., Chen, Y., & Fan, Q. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119421 · Cited by 97
Metadata
- Authors
- Emani, P. S., Warrell, J., Anticevic, A., Bekiranov, S., Gandal, M., McConnell, M. J., Sapiro, G., Aspuru-Guzik, A., Baker, J. T., Bastiani, M., Murray, J. D., Sotiropoulos, S. N., Taylor, J., Senthil, G., Lehner, T., Gerstein, M. B., & Harrow, A. W.
- Affiliations
- Yale University (US)
- University of Virginia (US)
- University of California, Los Angeles (US)
- Lieber Institute for Brain Development (US)
- Duke University (US)
- Harvard University (US)
- University of Nottingham (GB)
- Nottingham University Hospitals NHS Trust (GB)
- National Institute of Standards and Technology (US)
- New York Genome Center (US)
- Massachusetts Institute of Technology (US)
- Corresponding author
- Lehner, Thomas
- Gerstein, Mark B.
- Harrow, Aram W.
- ORCID
- Venue
- Nature Methods
- Published
- 2021-01-04
- Volume
- 18
- Issue
- 7
- Pages
- 701-709
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC8254820
- OpenAlex
- W2987160512
- Cited by
- 163
- References
- 48
- Citations by year
- 2026: 21 · 2025: 31 · 2024: 32 · 2023: 36 · 2022: 24 · 2021: 17
- FWCI
- 9.63
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature Methods (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Repository@Nottingham (University of Nottingham) (acceptedVersion, other-oa)
- arXiv (Cornell University) (submittedVersion)
- Repository@Nottingham (University of Nottingham) (submittedVersion, cc-by)
- arXiv (Cornell University)
- Funders
- National Institutes of Health (5r01mh120093-04, UL1TR001863)
- Engineering and Physical Sciences Research Council (EP/L023067/1)
- National Institute of Mental Health (U01MH116492, R01MH121521, U01MH106882, R01‐MH120093, R01MH137578)
- National Institute of Allergy and Infectious Diseases (R01AI143916, R01AI134895)
- National Center for Advancing Translational Sciences
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Mathematics › Theoretical Computer Science › DNA and Biological Computing
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Keywords
- quantum computing, computational complexity, qubits, interdisciplinary research
- MeSH terms
- Big Data, Algorithms, Biological Science Disciplines, Computers, Computing Methodologies, Genetics, Humans, Nervous System Diseases, Quantum Theory, Sequence Analysis, Genomics, Neuroimaging
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
