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
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
Items 101–163 of 163
Olofin, I. (2026) · Discover Computing · doi:10.1007/s10791-026-09924-y · Cited by 2
Ankalaki, S., Hukkeri, G. S., & Bhatnagar, S. (2026) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-026-10553-3 · Cited by 3
Ugya, A. Y., & Meguellati, K. (2022) · Egyptian Journal of Basic and Applied Sciences · doi:10.1080/2314808x.2022.2082133 · Cited by 2
Romano, Y., Primack, H., Vaknin, T., Meirzada, I., Karpas, I., Furman, D., Tradonsky, C., & Ben-shlomi, R. (2024) · Quantum Machine Intelligence · doi:10.1007/s42484-023-00128-x · Cited by 2
Quantum Computing Algorithms for Nonlinear Optimization Problems
Vishwakarma, V. K. (2023) · Communications on Applied Nonlinear Analysis · doi:10.52783/cana.v30.279 · Cited by 3
Wojciechowski, K., Kurowski, K., & Mazurek, C. (2024) · Postępy Biochemii · doi:10.18388/pb.2021_536 · Cited by 2
Designing quantum multi-category classifier from the perspective of brain processing information
Ding, X., Xu, J., Song, Z., Hou, Y., & Shan, Z. (2024) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ad7570 · Cited by 2
Probing fast quantum circuit refrigeration in the quantum regime
Nakamura, S., Yoshioka, T., Lemzyakov, S. A., Lvov, D., Mukai, H., Tomonaga, A., Takada, S., Okazaki, Y., Kaneko, N., Pekola, J. P., et al. (2025) · Physical Review Applied · doi:10.1103/physrevapplied.23.l011003 · Cited by 2
Simulating the Fermi-Hubbard model with long-range hopping on a quantum computer
Bespalova, T. A., Delić, K., Pupillo, G., Tacchino, F., & Tavernelli, I. (2025) · Physical Review A · doi:10.1103/physreva.111.052619 · Cited by 7
Understanding the Impact of Maternal Health on Neonatal Disease: A New Horizon
Maheshwari, A., Motta, M., & Lui, K. (2022) · Newborn · doi:10.5005/newborn-1-4-iv · Cited by 2
Population Health Management and the Science of Individuality in the Era of Quantum Computing
Moumtzoglou, A. S., & Pouliakis, A. (2021) · Advances in healthcare information systems and administration book series · doi:10.4018/978-1-7998-9198-7.ch002 · Cited by 2
Introduction to Synthetic Biology
Zhao, D. Y., & Ning, K. (2023) · Springer eBooks · doi:10.1007/978-981-99-2460-8_1 · Cited by 2
The Quantum Computer and the Security of Information Systems
Nouioua, T., & Hafid, B. A. (2021) · International Conference on Recent Advances in Mathematics and Informatics (ICRAMI) · doi:10.1109/icrami52622.2021.9585929 · Cited by 2
Quantum Encoding and Analysis on Continuous Time Stochastic Process with Financial ApplicationsPreprint
Zhuang, X., Chen, Z., Xue, C., Wu, Y.-C., & Guo, G. (2022) · RePEc: Research Papers in Economics · doi:10.48550/arxiv.2208.02364 · Cited by 2
Chen, Y., Huang, J.-H., Sun, Y., Zhang, Y., Li, Y., & Xu, X. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4601824 · Cited by 2
Overcoming Neuroanatomical Mapping and Computational Barriers in Human Brain Synaptic Architecture
Kumar, R., Waisberg, E., Ong, J., Paladugu, P., Amiri, D., & Jagadeesan, R. (2025) · Neuroinformatics · doi:10.1007/s12021-025-09715-8 · Cited by 3
Quantum Diamonds and the Future of Advanced Processors, Review on Benefits and Challenges
Vefaghi, M., Sedehi, H. R., & Ashkani, O. (2025) · Recent Progress in Materials · doi:10.21926/rpm.2503012 · Cited by 1
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
Quantum Computing in Healthcare: Exploring Mathematical Aspects, Algorithms and Applications
Singh, R., Nishad, D. K., Chaudhary, A., & Kumar, V. (2025) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-025-10461-y · Cited by 1
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
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
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
Shah, D. A., Younas, M. U., Jamali, Z. H., Lin, L., Zulfiqar, U., Alotaibi, M. S., Gururani, M. A., & Shahidin (2026) · Plant Stress · doi:10.1016/j.stress.2026.101528 · Cited by 1
Trannscriptomics and Immune Response in Human Cancer
Chaves, L. P., Melo, C. M., Lautert-Dutra, W., Caliari, A. L., & Squire, J. A. (2022) · Springer eBooks · doi:10.1007/978-3-030-87821-4_13 · Cited by 1
The Future Era of Computing: Automatic Computing
Singhal, S., & Pathak, N. (2023) · International Conference on Artificial Intelligence and Smart Communication (AISC) · doi:10.1109/aisc56616.2023.10085022 · Cited by 2
Influence of IQT on research in ICT
Bednarski, B., Lepak, Ł., Łyskawa, J., Pieńczuk, P., Rosoł, M., & Romaniuk, R. S. (2022) · International Journal of Electronics and Telecommunications · doi:10.24425/ijet.2022.139876 · Cited by 1
Perspective on the Role of Quantum Mechanical Calculations on Cellular Molecular Interactions
Elayyan, M., Sui, B., & Hoffmann, M. R. (2024) · Frontiers in computational chemistry · doi:10.2174/97898151790331240700005 · Cited by 1
Research on Quantum Circuit Learning Models for Molecular Dynamics Simulation
Nishida, Y. (2025) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.5c03624 · Cited by 1
The Security of Information Systems and Image Processing Supported by the Quantum Computer: A review
Nouioua, T., & Belbachir, A. H. (2022) · Advances in Science Technology and Engineering Systems Journal · doi:10.25046/aj070609 · Cited by 1
Halder, D., Prasannaa, V. S., Agarawal, V., & Maitra, R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.02864 · Cited by 2
Quantum gene regulatory networksPreprint
Cai, J. J., & Roman-Vicharra, C. (2022) · Research Square · doi:10.21203/rs.3.rs-1785614/v1 · Cited by 1
Harnessing quantum information to advance computing
(2024) · Nature Computational Science · doi:10.1038/s43588-024-00628-1 · Cited by 1
Roosan, D., Samrose, S., Khan, R. I., Nirzhor, S. S. R., & Provencher, B. A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.27.661905 · Cited by 1
Fabrication of superconducting qubits and auxiliary devices with niobium base layer
Su, F., Yang, Z., Zhao, S., Hai-sheng, Y., Tian, Y., & Zhao, S.-P. (2022) · Acta Physica Sinica · doi:10.7498/aps.71.20211865
Kirilyuk, M., & Bocharov, N. (2022) · Journal of «Almaz – Antey» Air and Defence Corporation · doi:10.38013/2542-0542-2022-1-93-101
The Basics of Quantum Computing
Journal, I. (2022) · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · doi:10.55041/ijsrem14152
Chen, 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
reddy, R., & Santhisri, T. (2023) · Research Square · doi:10.21203/rs.3.rs-2396897/v1
RetractedDNA as a perfect quantum computer based on the quantum physics principlePreprint
Aroche, R. R., Ortiz-García, Y. M., Arellano, M. A. M., & Leal, A. R. (2024) · Research Square · doi:10.21203/rs.3.rs-3830073/v1
Cruz, M. A. V. (2024) · Precision Nanomedicine · doi:10.33218/001c.117460
Innovative Applications of Quantum Signal Processing in Network Security and Image Processing
He, J., Qi, Z., Zhang, L., & Wang, Z. (2024) · SPIN · doi:10.1142/s201032472440006x
Quantum Computing for Bioinformatics
Cinaglia, P., & Cannataro, M. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95502-7.00182-2
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
On a quantum-inspired kernel for classifying protein torsion anglesPreprint
Malik, A. J., & Ascher, D. B. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.05.668681
Mental Health Prediction Using Quantum Machine Learning Classifiers: A Comprehensive Analysis
Garg, J., Wadhwa, V. M., Pooname, Sharma, D., & Yadav, S. K. (2025) · International Conference on Computing for Sustainable Global Development · doi:10.23919/indiacom66777.2025.11115669
155Chapter 6 First Steps Toward Graph-Based and Quantum Tools for Omics Expression Analysis
Bertel, L. G. A., Vivas-Reyes, R., & Ganguly, S. (2025) · De Gruyter eBooks · doi:10.1515/9783111264349-006
Assessment of System Reliability Using Quantum Computers
Hazra, I., Silva, G. S. M., & Droguett, E. L. (2025) · doi:10.1002/9781394345731.ch12
Zhuang, Z., Yang, Z., Huang, Y., Zheng, Y., Shi, X., Guo, R., Huang, Z., Lin, Z., Zhuang, R., Xie, L., et al. (2025) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-2025-81
Utilizing quantum fingerprints in plant cells to evaluate plant productivity
Ranasinghe, U., Stressinger, A. L., Xu, G., Sarhan, Y., Harrington, F., Berry, J. O., & Thomay, T. O. (2025) · The Analyst · doi:10.1039/d5an00326a
Perrier, E., & Aboy, M. (2026) · Perspectives in law, business and innovation · doi:10.1007/978-981-92-3768-5_3
Global and public health implications for quantum computing
Haider, M. R., Sarwar, G., Rahman, M. H., & Rahman, A. E. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-34193-9.00008-9
Jangid, S. K., Singh, S. P., Haripriya, Acharjya, K., & Husain, S. (2026) · Lecture notes in electrical engineering · doi:10.1007/978-981-95-3389-3_13
Exploration of Key Challenges and Features of Quantum Computing
Tripathi, A., Mishra, G. R., Srivastava, G., Singh, S. K., & Dubey, V. (2026) · Springer proceedings in physics · doi:10.1007/978-3-032-15441-5_28
Shintani, S. A. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.02.716025
Unlocking the power of quantum computing in biomedical NLP for lung cancer diagnosis
Kesiku, C. Y., García-Zapirain, B., & Elmaghraby, A. S. (2026) · Quantum Machine Intelligence · doi:10.1007/s42484-026-00392-7 · Cited by 2
Revolutionizing genomic medicine with artificial intelligence and data analytics
Patra, P. P. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-27638-5.00013-4
Prospects and Applications of Quantum Technologies
Hrytsyk, V., Babii, O., Nazarkevych, M., Astapenya, V., & Astapenya, V. (2023) · Workshop on Cybersecurity Providing in Information and Telecommunication Systems (CPITS) · doi:10.55056/ceur-ws.org/vol-3550/short1.pdf
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
Machine Learning in Molecular Dynamics Applications in Medicinal Chemistry
Amin, S. A., Kar, S., & Piotto, S. (2026) · Royal Society of Chemistry eBooks · doi:10.1039/9781837070206-00153
Quantum Artificial Intelligence opportunities, challenges, threats, use case and future trends
Mirza, M. A., Thota, N. R., & Mirza, F. A. (2026) · London Journal of Research In Computer Science and Technology · doi:10.34257/ljrcst230653uk
Genç, S. (2026) · International Artificial Intelligence and Data Processing Symposium (IDAP) · doi:10.1109/idap71869.2026.11728068
Subhalakshmi, R. T., Nivaashini, M., Dhanasekaran, V., & Nayaka, R. J. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-1985-6.ch017
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
