PeopleofSuperintelligence

Researcher

Mohamed Hibat-Allah

Also known as Mohamed Hibat Allah

Mohamed Hibat-Allah is a physicist who applies language models to quantum many-body systems and combinatorial optimization problems. He studied physics at École Normale Supérieure de Lyon, earned an M.S. through Perimeter Scholars International in 2019, and completed a Ph.D. in physics at the University of Waterloo in 2023. In 2020 he was first author of "Recurrent Neural Network Wave Functions", which uses recurrent neural networks as variational wave functions for quantum spin models, and in 2021 he followed with "Variational Neural Annealing" in Nature Machine Intelligence. After a 2023–2024 fellowship at the Perimeter Institute, he co-authored the V-score benchmark that Science published in October 2024 from a collaboration of thirty researchers. At the University of Waterloo, he is an assistant professor in the Department of Applied Mathematics and a faculty affiliate of the Vector Institute.

Timeline

  1. 2017–2019

    Physics degrees from Lyon to Waterloo

    He earns a B.S. in physics at École Normale Supérieure de Lyon in 2017. In 2019, he completes an M.S. in physics through Perimeter Scholars International, a program of the Perimeter Institute and the University of Waterloo.

  2. 2020

    Recurrent neural network wave functions

    In 2020, he is first author of "Recurrent Neural Network Wave Functions" in Physical Review Research, written with Martin Ganahl, Lauren E. Hayward, Roger G. Melko and Juan Carrasquilla. The paper uses recurrent neural networks, an architecture behind advances in natural language processing, as variational wave functions and computes ground state energies, correlation functions and entanglement entropies for quantum spin models in one and two spatial dimensions.

  3. 2021

    Variational neural annealing

    In 2021, he is first author of "Variational Neural Annealing" with Estelle M. Inack, Roeland Wiersema, Roger G. Melko and Juan Carrasquilla, published in Nature Machine Intelligence. A variational annealing method built on recurrent neural networks searches for ground-state solutions of spin glass Hamiltonians and outperforms traditional simulated annealing in the asymptotic limit.

  4. 2023

    A physics PhD at Waterloo

    A Ph.D. in physics from the University of Waterloo follows in 2023, with Juan Carrasquilla as his PhD supervisor.

  5. 2023–2024

    A fellowship at Perimeter Institute

    From 2023 to 2024, he is a Perimeter Scholars International fellow at the Perimeter Institute for Theoretical Physics, before joining the Department of Applied Mathematics at the University of Waterloo.

  6. October 2024

    The V-score benchmark in Science

    In October 2024, Science publishes "Variational Benchmarks for Quantum Many-Body Problems", the work of a collaboration of thirty researchers that includes him. The paper introduces the V-score, a metric of variational accuracy obtained from the variational energy and its variance, to compare computational methods for many-body ground-state problems.

  7. As of October 2026

    Assistant professor at Waterloo

    He is an assistant professor in the Department of Applied Mathematics at the University of Waterloo and a faculty affiliate of the Vector Institute. His research combines language models with quantum and classical many-body physics, including combinatorial optimization, and he teaches the course Neural Networks (AMATH 449/CS 479/CS 679).

Career

  1. NowUniversity of WaterlooAssistant Professor
  2. NowVector InstituteFaculty affiliate

Education

  1. 2023University of WaterlooPh.D.
  2. 2019Perimeter Institute for Theoretical PhysicsM.S.
  3. 2019University of WaterlooM.S.
  4. 2017École Normale Supérieure de LyonB.S.

From the biography.

  1. Of 943 people shaping AI who worked at Google, 446 work there nowIncludes Vector Institute. Our estimates of where the 943 people shaping AI who worked at Google and Google DeepMind work now: 446 at Google and 452 across 278 other organizations.
  2. Nonprofit roles are 2.3 times as common in AI safetyIncludes Vector Institute. We estimate where people focused on safety, alignment and evaluation work. Nonprofit roles appear for 25.1% of safety people with known affiliations, compared with 11.0% in other areas.
  3. Universities (31%) and startups and other companies (25%) employ the largest shares of 2020–2026 entrantsIncludes Vector Institute. Among 819 people whose first recorded role started in 2020–2026 and who have a known current organization, 60% work in commercial sectors (25% at startups and other companies, 23% at frontier labs and 13% at big tech, with 9 in two), and 31% work at universities, compared with 66% for earlier entrants.
  4. The United States employs an estimated 48% of people shaping AIIncludes Vector Institute. We estimate where the people shaping AI work today, how universities, companies and research institutes split across countries, and how country shares have changed since the 1960s.

Colleagues

People named in this biography, and people with dated positions at the same organization in the same years.

Sources and credits

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  1. Academic background - Mohamed Hibat-Allah mhibatallah.github.io. Checked 21 October 2026.
  2. Recurrent Neural Network Wave Functions (arXiv:2002.02973) arxiv.org. Checked 21 October 2026.
  3. Variational Neural Annealing (arXiv:2101.10154) arxiv.org. Checked 21 October 2026.
  4. Mohamed Hibat-Allah mhibatallah.github.io. Checked 21 October 2026.
  5. Science paper co-authored by Mohamed Hibat Allah receives widespread media coverage uwaterloo.ca. Checked 21 October 2026.
  6. Mohamed Hibat-Allah | Applied Mathematics | University of Waterloo uwaterloo.ca. Checked 21 October 2026.
  7. Variational Benchmarks for Quantum Many-Body Problems (arXiv:2302.04919) arxiv.org. Checked 21 October 2026.
  8. Mohamed Hibat-Allah - Vector Institute for Artificial Intelligence vectorinstitute.ai. Checked 21 October 2026.