Muhammad Usman

2.1k total citations
82 papers, 1.3k citations indexed

About

Muhammad Usman is a scholar working on Atomic and Molecular Physics, and Optics, Electrical and Electronic Engineering and Materials Chemistry. According to data from OpenAlex, Muhammad Usman has authored 82 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 45 papers in Atomic and Molecular Physics, and Optics, 33 papers in Electrical and Electronic Engineering and 27 papers in Materials Chemistry. Recurrent topics in Muhammad Usman's work include Semiconductor Quantum Structures and Devices (29 papers), Quantum Computing Algorithms and Architecture (23 papers) and Quantum and electron transport phenomena (18 papers). Muhammad Usman is often cited by papers focused on Semiconductor Quantum Structures and Devices (29 papers), Quantum Computing Algorithms and Architecture (23 papers) and Quantum and electron transport phenomena (18 papers). Muhammad Usman collaborates with scholars based in Australia, United States and Pakistan. Muhammad Usman's co-authors include Christopher A. Broderick, Eoin P. O’Reilly, Andrew J. Lindsay, Gerhard Klimeck, Lloyd C. L. Hollenberg, Abdul Samad Mumtaz, S. K. Hasanain, Sarah Erfani, M. E. Sevior and Maaz Khan and has published in prestigious journals such as Physical Review Letters, SHILAP Revista de lepidopterología and Applied Physics Letters.

In The Last Decade

Muhammad Usman

78 papers receiving 1.3k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Muhammad Usman Australia 20 762 565 467 265 167 82 1.3k
Stephanie Simmons Canada 22 1.4k 1.8× 830 1.5× 486 1.0× 786 3.0× 51 0.3× 45 2.0k
Hoon Ryu South Korea 14 942 1.2× 889 1.6× 341 0.7× 127 0.5× 79 0.5× 51 1.4k
Motomu Takatsu Japan 18 894 1.2× 728 1.3× 251 0.5× 279 1.1× 101 0.6× 51 1.3k
Suddhasatta Mahapatra India 15 1.1k 1.5× 1.1k 2.0× 419 0.9× 151 0.6× 78 0.5× 54 1.7k
Gian Salis Switzerland 26 2.2k 2.9× 744 1.3× 360 0.8× 466 1.8× 790 4.7× 79 2.6k
Rajib Rahman United States 29 1.8k 2.4× 1.9k 3.3× 836 1.8× 432 1.6× 182 1.1× 120 2.9k
Kunpeng Wang China 17 260 0.3× 108 0.2× 232 0.5× 208 0.8× 47 0.3× 72 811
Giles Allison Japan 16 1.1k 1.5× 662 1.2× 180 0.4× 475 1.8× 124 0.7× 35 1.3k
Riccardo Pisoni Switzerland 15 639 0.8× 401 0.7× 1.1k 2.4× 81 0.3× 61 0.4× 27 1.3k
Hao Tang China 18 579 0.8× 405 0.7× 222 0.5× 333 1.3× 33 0.2× 62 1.2k

Countries citing papers authored by Muhammad Usman

Since Specialization
Citations

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

Fields of papers citing papers by Muhammad Usman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Muhammad Usman

This figure shows the co-authorship network connecting the top 25 collaborators of Muhammad Usman. A scholar is included among the top collaborators of Muhammad Usman 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 Muhammad Usman. Muhammad Usman 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.
Sevior, M. E., et al.. (2025). Stabilizer Tensor Networks with Magic State Injection. Physical Review Letters. 134(19). 190602–190602. 7 indexed citations
2.
Sevior, M. E., et al.. (2025). Crosstalk Attacks and Defence in a Shared Quantum Computing Environment. Advanced Quantum Technologies. 8(10). 2 indexed citations
3.
Hoang, Thong, Xiwei Xu, Zhenchang Xing, et al.. (2025). Architectural patterns for designing quantum artificial intelligence systems. Journal of Systems and Software. 227. 112456–112456. 1 indexed citations
4.
Wang, Zeheng, et al.. (2025). Self‐Adaptive Quantum Kernel Principal Component Analysis for Compact Readout of Chemiresistive Sensor Arrays. Advanced Science. 12(15). e2411573–e2411573. 2 indexed citations
5.
Yeter‐Aydeniz, Kübra, et al.. (2025). Quantum generative learning for high-resolution medical image generation. Machine Learning Science and Technology. 6(2). 25032–25032. 2 indexed citations
6.
Kam, John B., et al.. (2025). Detrimental non-Markovian errors for surface code memory. Quantum Science and Technology. 10(3). 35060–35060. 1 indexed citations
7.
AlObaid, Abeer A., Muhammad Usman, Raqiqa Tur Rasool, et al.. (2024). Sol-gel synthesis of Cobalt-Bismuth substituted strontium hexaferrites and insight into structural refinement, Microstructural, dielectric and spectroscopic properties. Materials Science and Engineering B. 305. 117413–117413. 5 indexed citations
8.
Nguyen, Hoa T., Muhammad Usman, & Rajkumar Buyya. (2024). DRLQ: A Deep Reinforcement Learning-based Task Placement for Quantum Cloud Computing. 475–481.
9.
Sevior, M. E., et al.. (2024). Provably Trainable Rotationally Equivariant Quantum Machine Learning. PRX Quantum. 5(3). 16 indexed citations
10.
Usman, Muhammad, et al.. (2024). Quantum Transfer Learning with Adversarial Robustness for Classification of High‐Resolution Image Datasets. Advanced Quantum Technologies. 8(1). 2 indexed citations
11.
Nguyen, Hoa T., Muhammad Usman, & Rajkumar Buyya. (2024). iQuantum: A toolkit for modeling and simulation of quantum computing environments. Software Practice and Experience. 54(6). 1141–1171. 6 indexed citations
12.
Sevior, M. E., et al.. (2023). Boosted Ensembles of Qubit and Continuous Variable Quantum Support Vector Machines for B Meson Flavor Tagging. Advanced Quantum Technologies. 6(10). 9 indexed citations
13.
Erfani, Sarah, et al.. (2023). Benchmarking adversarially robust quantum machine learning at scale. Physical Review Research. 5(2). 28 indexed citations
14.
Nguyen, Hoa T., Muhammad Usman, & Rajkumar Buyya. (2023). iQuantum: A Case for Modeling and Simulation of Quantum Computing Environments. 21–30. 6 indexed citations
15.
Battistel, Francesco, et al.. (2023). Real-time decoding for fault-tolerant quantum computing: progress, challenges and outlook. Nano Futures. 7(3). 32003–32003. 29 indexed citations
16.
Hollenberg, Lloyd C. L., et al.. (2023). A scalable and fast artificial neural network syndrome decoder for surface codes. Quantum. 7. 1058–1058. 19 indexed citations
17.
Voisin, B., Joe Salfi, Muhammad Usman, et al.. (2022). Valley population of donor states in highly strained silicon. arXiv (Cornell University). 2(2). 25002–25002. 2 indexed citations
18.
Usman, Muhammad. (2019). Atomistic tight binding study of quantum confined Stark effect in GaBi x As 1− x /GaAs quantum wells. Journal of Physics Condensed Matter. 31(41). 415503–415503. 2 indexed citations
19.
Salfi, Joe, Juanita Bocquel, B. Voisin, et al.. (2018). Two-electron states of a group-V donor in silicon from atomistic full configuration interactions. Physical review. B.. 97(19). 17 indexed citations
20.
Broderick, Christopher A., S. Mazzucato, H. Carrère, et al.. (2014). Anisotropic electrongfactor as a probe of the electronic structure ofGaBixAs1−x/GaAsepilayers. Physical Review B. 90(19). 28 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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