Tameem Albash

5.0k total citations · 1 hit paper
58 papers, 3.0k citations indexed

About

Tameem Albash is a scholar working on Artificial Intelligence, Atomic and Molecular Physics, and Optics and Nuclear and High Energy Physics. According to data from OpenAlex, Tameem Albash has authored 58 papers receiving a total of 3.0k indexed citations (citations by other indexed papers that have themselves been cited), including 42 papers in Artificial Intelligence, 34 papers in Atomic and Molecular Physics, and Optics and 10 papers in Nuclear and High Energy Physics. Recurrent topics in Tameem Albash's work include Quantum Computing Algorithms and Architecture (38 papers), Quantum Information and Cryptography (38 papers) and Quantum and electron transport phenomena (21 papers). Tameem Albash is often cited by papers focused on Quantum Computing Algorithms and Architecture (38 papers), Quantum Information and Cryptography (38 papers) and Quantum and electron transport phenomena (21 papers). Tameem Albash collaborates with scholars based in United States, Canada and Japan. Tameem Albash's co-authors include Daniel A. Lidar, Clifford V. Johnson, Paolo Zanardi, Sergio Boixo, Itay Hen, Kristen L. Pudenz, Federico M. Spedalieri, Veselin G. Filev, Arnab Kundu and Walter Vinci and has published in prestigious journals such as Physical Review Letters, Nature Communications and Reviews of Modern Physics.

In The Last Decade

Tameem Albash

54 papers receiving 2.9k citations

Hit Papers

Adiabatic quantum computa... 2018 2026 2020 2023 2018 250 500 750
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Adiabatic quantum computation
    2018 863 citations Tameem Albash, Daniel A. Lidar Reviews of Modern Physics profile →

Author Peers

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

Author Last Decade Papers Cites
Tameem Albash 2.2k 1.5k 498 473 428 58 3.0k
Patrick Hayden 2.3k 1.0× 2.7k 1.8× 691 1.4× 927 2.0× 568 1.3× 58 3.7k
Stephen P. Jordan 1.2k 0.6× 924 0.6× 328 0.7× 286 0.6× 196 0.5× 49 1.8k
Matthew B. Hastings 2.1k 1.0× 3.2k 2.1× 266 0.5× 616 1.3× 150 0.4× 68 4.2k
Joshua M. Lapan 1.1k 0.5× 698 0.5× 238 0.5× 185 0.4× 127 0.3× 13 1.5k
Jeongwan Haah 1.3k 0.6× 2.2k 1.5× 233 0.5× 465 1.0× 64 0.1× 48 2.8k
Paul D. Nation 2.6k 1.2× 3.2k 2.1× 72 0.1× 409 0.9× 70 0.2× 18 3.9k
Markus Müller 2.6k 1.2× 3.5k 2.3× 113 0.2× 407 0.9× 33 0.1× 105 4.4k
Armin Uhlmann 2.2k 1.0× 2.5k 1.6× 98 0.2× 626 1.3× 75 0.2× 58 3.1k
Jordan Cotler 535 0.2× 528 0.3× 374 0.8× 346 0.7× 289 0.7× 35 1.1k
M. A. Martín-Delgado 2.8k 1.3× 4.7k 3.0× 189 0.4× 644 1.4× 59 0.1× 136 5.7k

Countries citing papers authored by Tameem Albash

Since Specialization
Citations

This map shows the geographic impact of Tameem Albash'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 Tameem Albash with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tameem Albash more than expected).

Fields of papers citing papers by Tameem Albash

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tameem Albash. 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 Tameem Albash. The network helps show where Tameem Albash may publish in the future.

Co-authorship network of co-authors of Tameem Albash

This figure shows the co-authorship network connecting the top 25 collaborators of Tameem Albash. A scholar is included among the top collaborators of Tameem Albash 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 Tameem Albash. Tameem Albash 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.
Albash, Tameem, et al.. (2025). Macroproperties vs. microstates in the classical simulation of critical phenomena in quench dynamics of 1D Ising models. New Journal of Physics. 27(1). 13026–13026. 1 indexed citations
2.
Rege, Ashutosh, et al.. (2024). Hamiltonian learning using machine-learning models trained with continuous measurements. Physical Review Applied. 22(4).
3.
Albash, Tameem, et al.. (2023). Decoherence limiting the cost to simulate an anharmonic oscillator. Physical review. A. 108(6). 3 indexed citations
4.
Smith, Conor, et al.. (2023). Quantum-inspired tempering for ground state approximation using artificial neural networks. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information).
5.
Sauvage, Frédéric, et al.. (2023). Diabatic quantum annealing for the frustrated ring model. Quantum Science and Technology. 8(4). 45033–45033. 8 indexed citations
6.
Vuffray, Marc, et al.. (2023). Signatures of Open and Noisy Quantum Systems in Single-Qubit Quantum Annealing. Physical Review Applied. 19(3). 7 indexed citations
7.
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
8.
Saguia, A., et al.. (2021). Localization transition induced by programmable disorder. arXiv (Cornell University). 4 indexed citations
9.
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
10.
Albash, Tameem & Daniel A. Lidar. (2018). Adiabatic quantum computation. Reviews of Modern Physics. 90(1). 863 indexed citations breakdown →
11.
Albash, Tameem, et al.. (2018). Finite temperature quantum annealing solving exponentially small gap problem with non-monotonic success probability. Nature Communications. 9(1). 2917–2917. 30 indexed citations
12.
Albash, Tameem & Daniel A. Lidar. (2017). Evidence for a Limited Quantum Speedup on a Quantum Annealer. arXiv (Cornell University). 4 indexed citations
13.
Albash, Tameem, V. Martı́n-Mayor, & Itay Hen. (2017). Temperature Scaling Law for Quantum Annealing Optimizers. Physical Review Letters. 119(11). 110502–110502. 36 indexed citations
14.
Albash, Tameem, et al.. (2017). Off-diagonal expansion quantum Monte Carlo. Physical review. E. 96(6). 63309–63309. 10 indexed citations
15.
Matsuura, Shunji, Hidetoshi Nishimori, Tameem Albash, & Daniel A. Lidar. (2016). Mean Field Analysis of Quantum Annealing Correction. Physical Review Letters. 116(22). 220501–220501. 25 indexed citations
16.
Vinci, Walter, et al.. (2014). Distinguishing Classical and Quantum Models for the D-Wave Device. arXiv (Cornell University). 17 indexed citations
17.
Pudenz, Kristen L., Tameem Albash, & Daniel A. Lidar. (2014). Error-corrected quantum annealing with hundreds of qubits. Nature Communications. 5(1). 3243–3243. 130 indexed citations
18.
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
19.
Boixo, Sergio, Tameem Albash, Federico M. Spedalieri, Nicholas Chancellor, & Daniel A. Lidar. (2013). Experimental signature of programmable quantum annealing. Nature Communications. 4(1). 2067–2067. 197 indexed citations
20.
Albash, Tameem, et al.. (2013). Fluctuation theorems for quantum processes. Physical Review E. 88(3). 32146–32146. 80 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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