Jacob Daniel Biamonte

FInstP
Born (1979-01-22) January 22, 1979
Lancaster, New Hampshire, United States
NationalityAmerican
EducationB.S. (2004), Ph.D. (2010), D.Sc. (2022)
Alma materPortland State University
University of Oxford
Moscow Institute of Physics and Technology
Known forAdiabatic Quantum Computing, Quantum Machine Learning
Awards USERN Medal, Fellow IMA
Scientific career
FieldsQuantum Computing
Tensor Networks
Mathematical Physics
InstitutionsSkolkovo Institute of Science and Technology
Harvard University
University of Oxford

Jacob Daniel Biamonte FInstP is an American physicist and theoretical computer scientist active in the fields of quantum information theory and quantum computing. He left a tenured professorship at the Skolkovo Institute of Science and Technology in Russia[1] after the start of the Russo-Ukrainian War.

Biamonte contributed several universality proofs which established the first experimentally relevant universal models of adiabatic quantum computation. He also proved universality of the NISQ era variational model of quantum computation[2] and published several results in the development of quantum machine learning[3] and the mathematics of tensor networks.

Education

Biamonte completed a Ph.D. at the University of Oxford in 2010.[4] In 2022 he defended a thesis for Russia's Doctor of Physical and Mathematical Sciences at Moscow Institute of Physics and Technology.[5][6]

Honors and awards

In 2023 Biamonte was elected Fellow of the Institute of Physics and in 2021 he became a Fellow of the Institute of Mathematics and its Applications. In 2018 Biamonte was awarded the USERN Medal in Formal Sciences for his work on quantum algorithms.[7] In 2014 Biamonte became an invited member of the Foundational Questions Institute.[8][9]

References

  1. ↑ "Faculty Profile at the Skolkovo Institute of Science and Technology". skoltech.ru. Retrieved May 9, 2022.
  2. ↑ Biamonte, Jacob (2021). "Universal variational quantum computation". Physical Review A. 103 (3): L030401. arXiv:1903.04500. doi:10.1103/PhysRevA.103.L030401.
  3. ↑ Biamonte, Jacob; Wittek, Peter; Nicola, Pancotti; Rebentrost, Patrick; Wiebe, Nathan; Lloyd, Seth (2017). "Quantum machine learning". Nature. 549 (7671): 195–202. arXiv:1611.09347. doi:10.1038/nature23474. PMID 28905917. S2CID 64536201.
  4. ↑ "Mathematics Genealogy Project". genealogy.math.ndsu.nodak.edu. Retrieved May 9, 2022.
  5. ↑ "Moscow Institute of Physics and Technology Dissertation Council". mipt.ru. Retrieved May 9, 2022.
  6. ↑ Biamonte, Jacob (2022). On the mathematical structure of quantum models of computation based on Hamiltonian minimisation (DSc). Moscow Institute of Physics and Technology. p. 242. arXiv:2009.10088.
  7. ↑ "The 2018 USERN Prize Ceremony in Reggio Calabria". usern.tums.ac.ir. Retrieved May 12, 2022.
  8. ↑ "Awarded Projects Announcements". fqxi.org. Retrieved May 12, 2022.
  9. ↑ "Six Degrees to the Emergence of Reality, FQXi interview, by Carinne Piekema". fqxi.org. Retrieved May 12, 2022.
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