AS6-05-20


eanschuetz.github.io

Eric ANSCHUETZ

NUS Presidential Young Professor
Joint appointment with Department of Physics, NUS

  • Ph.D. (Physics, Massachusetts Institute of Technology, 2023)
  • A.M. (Physics, Harvard University, 2017)
  • A.B. (Physics and Mathematics joint concentration, Computer Science secondary, Harvard University, 2017)

I am an NUS Presidential Young Professor at NUS with a joint appointment between the Departments of Computer Science and Physics. I previously completed my Ph.D. in Physics at MIT under the supervision of Aram Harrow and Mikhail Lukin, and prior to joining NUS was a Burke Fellow at Caltech. I use methods from statistical physics to understand the average-case complexity of optimisation, learning, and sampling problems, both for traditional ("classical") algorithms and for algorithms which take advantage of quantum mechanical effects ("quantum algorithms"). I also work on understanding what features of a problem might make it more amenable to quantum algorithms than classical algorithms using ideas from quantum foundations theory.

RESEARCH AREAS

Algorithms & Theory
  • Optimisation
  • Quantum Information & Algorithms
Artificial Intelligence
  • Machine Learning

RESEARCH INTERESTS

  • Quantum algorithmic hardness

  • Quantum optimisation

  • Spin glass theory

  • Quantum machine learning theory

RESEARCH PROJECTS

RESEARCH GROUPS

TEACHING INNOVATIONS

SELECTED PUBLICATIONS

  • Eric R. Anschuetz, "Quantum Glassiness from Efficient Learning," Commun. Math. Phys. 407, 30 (2026), Quantum Information Processing (2026)
  • Eric R. Anschuetz, "A Unified Theory of Quantum Neural Network Loss Landscapes," in International Conference on Learning Representations (2026) pp. 97859–97918
  • Eric R. Anschuetz, Chi-Fang Chen, Bobak T. Kiani, and Robbie King, "Strongly Interacting Fermions Are Nontrivial yet Nonglassy," Phys. Rev. Lett. 135, 030602 (2025), Quantum Information Processing (2025)
  • Eric R. Anschuetz and Bobak T. Kiani, "Quantum variational algorithms are swamped with traps," Nat. Commun. 13, 7760 (2022)
  • Eric R. Anschuetz, "Critical Points in Quantum Generative Models," in International Conference on Learning Representations (2022)

AWARDS & HONOURS

  • Sherman Fairchild Prize Postdoctoral Fellowship, Burke Fellow, California Institute of Technology (2023)

  • Graduate Research Fellow, National Science Foundation (2017)

  • Dean of Science Fellow, Massachusetts Institute of Technology (2017)

COURSES TAUGHT