World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
60
Citations
19926
World Ranking
2129
National Ranking
85

Nathan Wiebe publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Nathan Wiebe sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 179 publications — 39th percentile

39% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Nathan Wiebe D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Nathan Wiebe sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 60 D-Index — 78th percentile

78% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Overview

Nathan Wiebe is a researcher affiliated with the University of Toronto in Canada. Their work spans a range of topics primarily in the fields of computer science and physics and astronomy, focusing heavily on quantum computing and its associated disciplines.

Their research contributions cover several main fields of study including:

  • Computer Science
  • Physics and Astronomy

Within these broad fields, their subfields of study include:

  • Artificial Intelligence
  • Atomic and Molecular Physics, and Optics
  • Computational Theory and Mathematics
  • Electrical and Electronic Engineering
  • Nuclear and High Energy Physics

Their primary research topics involve quantum computing methodologies and phenomena, as reflected in the following main topics of work:

  • Quantum Computing Algorithms and Architecture
  • Quantum Information and Cryptography
  • Quantum and electron transport phenomena
  • Quantum many-body systems
  • Quantum-Dot Cellular Automata
  • Quantum Mechanics and Applications
  • Spectroscopy and Quantum Chemical Studies

Nathan Wiebe has published extensively, with frequent appearances in specific venues. The venues where they have published multiple works include:

  • arXiv (Cornell University)
  • PRX Quantum
  • Quantum
  • Physical Review Research
  • 2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)

Their recent papers illustrate the scope and themes of their research. Notable publications include:

  • "Circuit-centric quantum classifiers," published in 2020 in Physical Review A/Physical Review, A
  • "Entanglement-Induced Barren Plateaus," published in 2023 in OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)
  • "Quantum Simulation for High-Energy Physics," published in 2023 in PRX Quantum
  • "Drug design on quantum computers," published in 2024 in Nature Physics
  • "Real-Time Evolution for Ultracompact Hamiltonian Eigenstates on Quantum Hardware," published in 2022 in PRX Quantum

In their collaborative work, Nathan Wiebe has frequently co-authored with several researchers, indicating sustained partnerships with:

  • Raffaele Santagati
  • Alessandro Roggero
  • Matthias Degroote
  • Nikolaj Moll
  • Michael Streif

Best Publications

  • Quantum machine learning

    Jacob D. Biamonte;Jacob D. Biamonte;Peter Wittek;Nicola Pancotti;Patrick Rebentrost

  • Circuit-centric quantum classifiers

    Maria Schuld;Alex Bocharov;Krysta M. Svore;Nathan Wiebe;Nathan Wiebe;Nathan Wiebe

  • Hartree-Fock on a superconducting qubit quantum computer

    Frank Arute;Kunal Arya

  • Elucidating reaction mechanisms on quantum computers

    Markus Reiher;Nathan Wiebe;Krysta M. Svore;Dave Wecker

  • Hartree-Fock on a superconducting qubit quantum computer

    Frank Arute;Kunal Arya;Ryan Babbush;Dave Bacon

  • Quantum Algorithm for Data Fitting

    Nathan Wiebe;Daniel Braun;Daniel Braun;Seth Lloyd

  • A Theory of Trotter Error.

    Andrew M. Childs;Yuan Su;Minh C. Tran;Nathan Wiebe

  • Quantum Simulation of Electronic Structure with Linear Depth and Connectivity

    Ian D. Kivlichan;Ian D. Kivlichan;Jarrod McClean;Nathan Wiebe;Craig Michael Gidney

  • Quantum singular value transformation and beyond: exponential improvements for quantum matrix arithmetics

    András Gilyén;Yuan Su;Guang Hao Low;Nathan Wiebe

  • Encoding Electronic Spectra in Quantum Circuits with Linear T Complexity

    Ryan Babbush;Craig Michael Gidney;Dominic W. Berry;Nathan Wiebe

  • Low-Depth Quantum Simulation of Materials

    Ryan Babbush;Nathan Wiebe;Jarrod McClean;James McClain

  • Solving strongly correlated electron models on a quantum computer

    Dave Wecker;Matthew B. Hastings;Nathan Wiebe;Bryan K. Clark;Bryan K. Clark

  • Hamiltonian simulation using linear combinations of unitary operations

    Andrew M. Childs;Nathan Wiebe

  • Hamiltonian Simulation Using Linear Combinations of Unitary Operations

    Nathan Wiebe;Andrew Childs

  • Experimental quantum Hamiltonian learning

    Jianwei Wang;Stefano Paesani;Raffaele Santagati;Sebastian Knauer

  • Efficient and noise resilient measurements for quantum chemistry on near-term quantum computers

    William J. Huggins;William J. Huggins;Jarrod R. McClean;Nicholas C. Rubin;Zhang Jiang

  • Even more efficient quantum computations of chemistry through tensor hypercontraction

    Joonho Lee;Dominic W. Berry;Craig Michael Gidney;William J. Huggins

  • Tomography and generative training with quantum Boltzmann machines

    Mária Kieferová;Nathan Wiebe

  • Hamiltonian learning and certification using quantum resources.

    Nathan Wiebe;Nathan Wiebe;Christopher Granade;Christopher Ferrie;D. G. Cory

  • Quantum algorithms for nearest-neighbor methods for supervised and unsupervised learning

    Nathan Wiebe;Ashish Kapoor;Krysta M. Svore

  • Witnessing eigenstates for quantum simulation of Hamiltonian spectra.

    Raffaele Santagati;Jianwei Wang;Antonio A. Gentile;Stefano Paesani

  • Low Depth Quantum Simulation of Electronic Structure

    Ryan Babbush;Nathan Wiebe;Jarrod McClean;James McClain

  • Robust Online Hamiltonian Learning.

    Christopher E. Granade;Christopher Ferrie;Nathan Wiebe;David G. Cory

  • Elucidating Reaction Mechanisms on Quantum Computers

    Nathan Wiebe;Markus Reiher;Krysta Svore;Dave Wecker

Frequent Co-Authors

Mark G. Thompson
Mark G. Thompson University of Bristol
Krysta M. Svore
Krysta M. Svore Microsoft (United States)
Matthias Troyer
Matthias Troyer Microsoft (United States)
John Rarity
John Rarity University of Bristol
Jeremy L. O'Brien
Jeremy L. O'Brien University of Bristol
Fabio Sciarrino
Fabio Sciarrino Sapienza University of Rome
Ashish Kapoor
Ashish Kapoor Microsoft (United States)
Hartmut Neven
Hartmut Neven Google (United States)
David P. Tew
David P. Tew University of Oxford
David G. Cory
David G. Cory University of Waterloo

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