Daniel A. Lidar

23.1k total citations · 4 hit papers
242 papers, 14.9k citations indexed

About

Daniel A. Lidar is a scholar working on Artificial Intelligence, Atomic and Molecular Physics, and Optics and Computational Theory and Mathematics. According to data from OpenAlex, Daniel A. Lidar has authored 242 papers receiving a total of 14.9k indexed citations (citations by other indexed papers that have themselves been cited), including 212 papers in Artificial Intelligence, 174 papers in Atomic and Molecular Physics, and Optics and 22 papers in Computational Theory and Mathematics. Recurrent topics in Daniel A. Lidar's work include Quantum Information and Cryptography (194 papers), Quantum Computing Algorithms and Architecture (190 papers) and Quantum and electron transport phenomena (79 papers). Daniel A. Lidar is often cited by papers focused on Quantum Information and Cryptography (194 papers), Quantum Computing Algorithms and Architecture (190 papers) and Quantum and electron transport phenomena (79 papers). Daniel A. Lidar collaborates with scholars based in United States, Canada and Israel. Daniel A. Lidar's co-authors include Tameem Albash, K. Birgitta Whaley, Lian-Ao Wu, Isaac L. Chuang, M. S. Sarandy, Kaveh Khodjasteh, Paolo Zanardi, Dave Bacon, Alireza Shabani and Ofer Biham and has published in prestigious journals such as Nature, Science and Chemical Reviews.

In The Last Decade

Daniel A. Lidar

239 papers receiving 14.4k citations

Hit Papers

Decoherence-Free Subspace... 1998 2026 2007 2016 1998 2018 2014 2014 400 800 1.2k

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Daniel A. Lidar 11.9k 10.8k 1.4k 1.1k 898 242 14.9k
Francesco Petruccione 6.4k 0.5× 6.6k 0.6× 2.1k 1.5× 577 0.5× 605 0.7× 248 9.5k
Vlatko Vedral 20.8k 1.7× 23.6k 2.2× 4.5k 3.1× 552 0.5× 1.3k 1.5× 325 26.6k
Raymond Laflamme 13.4k 1.1× 12.1k 1.1× 1.1k 0.8× 1.7k 1.5× 2.3k 2.5× 179 17.4k
Vittorio Giovannetti 14.0k 1.2× 15.3k 1.4× 3.1k 2.1× 465 0.4× 2.0k 2.2× 299 18.4k
A. N. Cleland 7.9k 0.7× 12.5k 1.2× 826 0.6× 484 0.4× 4.3k 4.8× 136 15.0k
John M. Martinis 12.7k 1.1× 16.9k 1.6× 1.5k 1.0× 905 0.8× 4.4k 4.9× 245 22.1k
Kae Nemoto 6.6k 0.6× 7.2k 0.7× 524 0.4× 291 0.3× 1.4k 1.6× 330 9.9k
Barry C. Sanders 10.2k 0.9× 11.5k 1.1× 1.1k 0.7× 579 0.5× 1.5k 1.6× 363 13.9k
G. J. Milburn 18.5k 1.6× 22.6k 2.1× 2.8k 2.0× 391 0.3× 4.4k 4.9× 311 25.5k
P. L. Knight 13.6k 1.1× 17.2k 1.6× 2.0k 1.4× 307 0.3× 1.3k 1.5× 219 19.1k

Countries citing papers authored by Daniel A. Lidar

Since Specialization
Citations

This map shows the geographic impact of Daniel A. Lidar's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Daniel A. Lidar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel A. Lidar more than expected).

Fields of papers citing papers by Daniel A. Lidar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel A. Lidar. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Daniel A. Lidar. The network helps show where Daniel A. Lidar may publish in the future.

Co-authorship network of co-authors of Daniel A. Lidar

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel A. Lidar. A scholar is included among the top collaborators of Daniel A. Lidar based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Daniel A. Lidar. Daniel A. Lidar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Lidar, Daniel A., et al.. (2025). Beyond unital noise in variational quantum algorithms: noise-induced barren plateaus and limit sets. Quantum. 9. 1617–1617. 3 indexed citations
2.
Zhou, Zeyuan, et al.. (2025). Demonstration of Algorithmic Quantum Speedup for an Abelian Hidden Subgroup Problem. Physical Review X. 15(2). 1 indexed citations
3.
Pokharel, Bibek & Daniel A. Lidar. (2024). Better-than-classical Grover search via quantum error detection and suppression. npj Quantum Information. 10(1). 11 indexed citations
4.
Albash, Tameem, et al.. (2022). 3-regular three-XORSAT planted solutions benchmark of classical and quantum heuristic optimizers. Quantum Science and Technology. 7(2). 25008–25008. 32 indexed citations
5.
Gujja, Sharvari, et al.. (2021). Quantum processor-inspired machine learning in the biomedical sciences. Patterns. 2(6). 100246–100246. 21 indexed citations
6.
Chen, Huo & Daniel A. Lidar. (2020). Why and When Pausing is Beneficial in Quantum Annealing. eScholarship (California Digital Library). 26 indexed citations
7.
Albash, Tameem, et al.. (2020). Limitations of error corrected quantum annealing in improving the performance of Boltzmann machines. Quantum Science and Technology. 5(4). 45010–45010. 16 indexed citations
8.
Zlokapa, Alexander, A. Mott, Joshua Job, et al.. (2020). Quantum adiabatic machine learning by zooming into a region of the energy surface. Physical review. A. 102(6). 17 indexed citations
9.
Gujja, Sharvari, et al.. (2019). Unconventional machine learning of genome-wide human cancer data. arXiv (Cornell University). 3 indexed citations
10.
Vinci, Walter & Daniel A. Lidar. (2017). Non-stoquastic Hamiltonians in quantum annealing via geometric phases. npj Quantum Information. 3(1). 24 indexed citations
11.
Albash, Tameem & Daniel A. Lidar. (2017). Evidence for a Limited Quantum Speedup on a Quantum Annealer. arXiv (Cornell University). 4 indexed citations
12.
Wang, Zhihui, Sergio Boixo, Tameem Albash, & Daniel A. Lidar. (2013). Benchmarking the D-Wave adiabatic quantum optimizer via 2D-Ising spin glasses. Bulletin of the American Physical Society. 2013. 1 indexed citations
13.
Lidar, Daniel A., A. T. Rezakhani, & Alioscia Hamma. (2008). Adiabatic approximation with better than exponential accuracy for many-body systems and quantum computation. arXiv (Cornell University). 2 indexed citations
14.
Grace, Matthew, Constantin Brif, Herschel Rabitz, et al.. (2007). Fidelity of optimally controlled quantum gates with randomly coupled multiparticle environments. Journal of Modern Optics. 54(16-17). 2339–2349. 17 indexed citations
15.
Lidar, Daniel A., et al.. (2006). Quantum logic gates in iodine vapor using time–frequency resolved coherent anti-Stokes Raman scattering: a theoretical study. Molecular Physics. 104(8). 1249–1266. 10 indexed citations
16.
Shabani, Alireza & Daniel A. Lidar. (2005). Theory of initialization-free decoherence-free subspaces and subsystems (14 pages). Physical Review A. 72(4). 42303. 6 indexed citations
17.
Zanardi, Paolo & Daniel A. Lidar. (2004). Purity and state fidelity of quantum channels (7 pages). Physical Review A. 70(1). 12315. 1 indexed citations
18.
Facchi, Paolo, Daniel A. Lidar, & Saverio Pascazio. (2003). Generalized Dynamical Decoupling from the Quantum Zeno Effect. arXiv (Cornell University). 1 indexed citations
19.
Lidar, Daniel A., et al.. (2001). Parafermionic Quantum Computation. arXiv (Cornell University). 1 indexed citations
20.
Biham, Eli, Ofer Biham, David Biron, Markus Grassl, & Daniel A. Lidar. (1998). Exact Solution of Grover's Quantum Search Algorithm for Arbitrary Initial Amplitude Distribution. arXiv (Cornell University). 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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