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Eric R. Anschuetz

Assistant Professor at NUS

Education

Burke Fellow

Caltech
2023 - 2026

Ph.D. in Physics

Massachusetts Institute of Technology
2017 - 2023

M.A. in Physics

Harvard University
2016 - 2017

B.A. in Physics and Mathematics

Harvard University
2013 - 2017

About

I’m an Assistant Professor at the National University of Singapore (NUS) working on the (quantum || classical) algorithmic implications of (quantum || classical) spin glass theory. Some of my past research applied these techniques to quantum machine learning algorithms, which I showed generally fail to train due to a proliferation of poor local minima in their optimization landscapes. Learning why algorithms break has also helped me design ones that don’t. I was recently awarded the Presidential Young Professorship here at NUS, with a joint appointment between the Departments of Physics and Computer Science.

Check out my CV if you want to see more details on some of my highlighted work or just more details about me in general.

Sometimes this page is out of date; check out my Google Scholar or my arXiv page to see what I’ve been up to recently!

I’m hiring! If you’re looking for a postdoc, Ph.D. position, or research assistantship, all in either physics or computer science, get in touch at e-ans@nus.edu.sg. For Ph.D. students, the physics application is here (due November 15th Singapore time, i.e., November 14th in the US), and the computer science application is here (due December 15th Singapore time). Just be sure to mention your name in your application so it gets routed to me.

Publications

  • The Quantum Overlap Gap Property and Algorithmic Hardness for the Quantum Hypergraph Max-Cut Problem
    • M. Mints, E.R. Anschuetz
    arXiv:2609.10838 [quant-ph]
  • Fragmentation is Efficiently Learnable by Quantum Neural Networks
    • M. Mints, E.R. Anschuetz
    arXiv:2512.00751 [quant-ph]
  • Average-case quantum complexity from glassiness
    • A. Zlokapa, B.T. Kiani, E.R. Anschuetz
    QIP 2026, arXiv:2510.08497 [quant-ph]
  • Decoded Quantum Interferometry Requires Structure
    • E.R. Anschuetz, D. Gamarnik, J.Z. Lu
    arXiv:2509.14509 [quant-ph]
  • k-Contextuality as a Heuristic for Memory Separations in Learning
    • M.H. Teo, W. Yang, J. Sud, T. Tomesh, F.T. Chong, E.R. Anschuetz
    IEEE International Conference on Quantum Computing and Engineering (QCE) (IEEE Computer Society, 2025) pp. 2399–2409
  • Optimizing Sparse SYK
    • M. Ding, R. King, B.T. Kiani, E.R. Anschuetz
    Quantum 10, 2029 (2026)
  • Quantum Glassiness From Efficient Learning
    • E.R. Anschuetz
    Commun. Math. Phys. 407, 30 (2026), QIP 2026
  • Strongly Interacting Fermions Are Nontrivial yet Nonglassy
    • E.R. Anschuetz, C.-F. Chen, B.T. Kiani, R. King
    Phys. Rev. Lett. 135, 030602 (2025), QIP 2025
  • A Unified Theory of Quantum Neural Network Loss Landscapes
    • E.R. Anschuetz
    International Conference on Learning Representations (2025)
  • Bounds on the Ground State Energy of Quantum p-Spin Hamiltonians
    • E.R. Anschuetz, D. Gamarnik, B.T. Kiani
    Commun. Math. Phys. 406, 232 (2025)
  • Q-CHOP: Quantum constrained Hamiltonian optimization
    • M.A. Perlin, R. Shaydulin, ..., E.R. Anschuetz, M. Pistoia, P. Gokhale
    ACM Trans. Quantum Comput. (2025)
  • Arbitrary Polynomial Separations in Trainable Quantum Machine Learning
    • E.R. Anschuetz, X. Gao
    Quantum 10, 1976 (2026)
  • Does provable absence of barren plateaus imply classical simulability?
    • M. Cerezo, M. Larocca, D. García-Martín, ..., E.R. Anschuetz, Z. Holmes
    Nat. Commun. 16, 7907 (2025)
  • The Trainability and Expressivity of Quantum Machine Learning Models
    • E.R. Anschuetz
    Ph.D. thesis
  • Combinatorial NLTS From the Overlap Gap Property
    • E.R. Anschuetz, D. Gamarnik, B. Kiani
    Quantum 8, 1527 (2024)
  • SupercheQ: Quantum Advantage for Distributed Databases
    • E.R. Anschuetz, P. Gokhale, B. Tonekaboni et al.
    Tech. Rep. (Infleqtion, 2022)
  • Efficient classical algorithms for simulating symmetric quantum systems
    • E.R. Anschuetz, A. Bauer, B.T. Kiani, S. Lloyd
    Quantum 7, 1189 (2023)
  • Training Quantum Boltzmann Machines with Coresets
    • J. Viszlai, T. Tomesh, P. Gokhale, E. Anschuetz, F.T. Chong
    IEEE International Conference on Quantum Computing and Engineering (QCE) (IEEE Computer Society, 2022) pp. 292–298
  • Interpretable Quantum Advantage in Neural Sequence Learning
    • E.R. Anschuetz, H.-Y. Hu, J.-L. Huang, X. Gao
    PRX Quantum 4, 020338 (2023)
  • Degeneracy engineering for classical and quantum annealing: A case study of sparse linear regression in collider physics
    • E.R. Anschuetz, L. Funcke, P.T. Komiske, S. Kryhin, J. Thaler
    Phys. Rev. D 106, 056008 (2022)
  • Quantum variational algorithms are swamped with traps
    • E.R. Anschuetz, B.T. Kiani
    Nat. Commun. 13, 7760 (2022)
  • ORQVIZ: Visualizing High-Dimensional Landscapes in Variational Quantum Algorithms
    • M.S. Rudolph, S. Sim, A. Raza, M. Stechly, J.R. McClean, E.R. Anschuetz, L. Serrano, A. Perodomo-Ortiz
    Tech. Rep. (Zapata Computing Inc., 2021)
  • Critical Points in Quantum Generative Models
    • E.R. Anschuetz
    International Conference on Learning Representations (2022)
  • Enhancing Generative Models via Quantum Correlations
    • X. Gao, E.R. Anschuetz, S.-T. Wang, J.I. Cirac, M.D. Lukin
    Phys. Rev. X 12, 021037 (2022)
  • Coreset Clustering on Small Quantum Computers
    • T. Tomesh, P. Gokhale, E.R. Anschuetz, F.T. Chong
    Electronics 10, 1690 (2021)
  • Using Spectral Graph Theory to Map Qubits onto Connectivity-limited Devices
    • J.X. Lin, E.R. Anschuetz, A.W. Harrow
    ACM Trans. Quantum Comput. 2, 1 (2021)
  • Near-term quantum-classical associative adversarial networks
    • E.R. Anschuetz, C. Zanoci
    Phys. Rev. A 100, 052327 (2019)
  • Realizing Quantum Boltzmann Machines Through Eigenstate Thermalization
    • E.R. Anschuetz, Y. Cao
    arxiv:1903.01359 [quant-ph]
    Results featured as an invited talk at the Quantum Machine Learning and Data Analytics Workshop (2019).
  • Variational Quantum Factoring
    • E. Anschuetz, J. Olson, A. Aspuru-Guzik, Y. Cao
    International Workshop on Quantum Technology and Optimization Problems (Springer, 2019) pp. 74–85
  • Atom-by-atom assembly of defect-free one-dimensional cold atom arrays
    • M. Endres, H. Bernien, A. Keesling, H. Levine, E.R. Anschuetz, A. Krajenbrink, C. Senko, V. Vuletic, M. Greiner, M.D. Lukin
    Science 354, 1024 (2016)

    Talks

  • Quantum Glassiness from Efficient Learning
    • Invited talk, Institute for Quantum Inspired and Quantum Optimization Seminar, Technische Universität Hamburg (2026)
  • Decoded Quantum Interferometry Requires Structure
    • Invited talk, Universal Quantum Computing Seminar, Centre for Quantum Technologies (2026)
  • Interpretable Quantum Advantage in Machine Learning from Contextuality
    • Invited talk, Nonlocality: Driver for Quantum Advantage?, Lorentz Center (2026)
  • Arbitrary Polynomial Separations in Trainable Quantum Machine Learning
    • Invited talk, Nanyang Quantum Hub seminar, Nanyang Technological University (2026)
  • What Goes Wrong in Quantum Optimization?
    • Invited talk, Microsoft seminar (2026)
  • Efficient Learning Implies Quantum Glassiness
    • Quantum Information Processing (2026)
  • Average-case quantum complexity from glassiness
    • Invited talk, International Workshop on Quantum Boltzmann Machines (2025)
  • A Unified Theory of Quantum Neural Network Loss Landscapes
    • Quantum Techniques in Machine Learning (QTML) (2025)
  • Efficient Learning Implies Quantum Glassiness
    • Invited talk, AIMS Workshop and School on The Theory of Quantum Learning Algorithms (2025)
  • Efficient Learning Implies Quantum Glassiness
    • Invited talk, Foxconn Quantum Computing Seminar (2025)
  • Decoded Quantum Interferometry Requires Structure
    • Invited talk, Caltech Theory Tea (2025)
  • Efficient Learning Implies Quantum Glassiness
    • Invited talk, Quantum Many-Body Seminar, F.U. Berlin (2025)
  • A Unified Theory of Quantum Neural Network Loss Landscapes
    • Theory of Quantum Computation, Communication and Cryptography (TQC) (2025)
  • A Unified Theory of Quantum Neural Network Loss Landscapes
    • Invited talk, Understanding Quantum Machine Learning Workshop (2025)
  • Efficient Learning Implies Quantum Glassiness
    • Invited talk, University of Bologna Seminar (2025)
  • Arbitrary Polynomial Separations in Trainable Quantum Machine Learning
    • Invited talk, Quantum Techniques in Machine Learning (QTML) (2024)
  • Arbitrary Polynomial Separations in Trainable Quantum Machine Learning
    • Invited talk, aQa seminar (2024)
  • A Unified Theory of Quantum Neural Network Loss Landscapes
    • Invited talk, Quantum Machine Learning seminar, Google (2024)
  • Rethinking Quantum Learning Algorithms
    • Invited keynote talk, MMM11 (2024)
  • A Unified Theory of Quantum Neural Network Loss Landscapes
    • Invited talk, Quantum Learning Seminar, F.U. Berlin (2024)
  • Arbitrary Polynomial Separations in Trainable Quantum Machine Learning
    • Invited talk, QED-C Quantum Talent Showcase (2024)
  • Arbitrary Polynomial Separations in Trainable Quantum Machine Learning
    • Invited talk, SeeQA (2024)
  • Arbitrary Polynomial Separations in Trainable Quantum Machine Learning
    • Invited talk, Los Alamos National Laboratory (2024)
  • Quantum Theory and Algorithms
    • Invited talk, Planning Workshop on Quantum Computing, ASPLOS (2024)
  • Rethinking Quantum Neural Networks
    • Invited talk, CSUN (2024)
  • Interpretable Expressivity Separations in Trainable Quantum Machine Learning
    • Invited talk, IQIM Seminar, Caltech (2024)
  • Enhancing Generative Models via Quantum Correlations
    • Invited talk, Tensor Network Reading Group, Mila (2023)
  • A discussion on QML
    • Panel, CIFAR Quantum Information Science Program Meeting (2023)
  • Interpretable Quantum Advantage in Neural Sequence Learning
    • Invited talk, Centre for Quantum Technologies (2023)
  • Efficient classical algorithms for simulating symmetric quantum systems
    • Invited talk, Centre for Quantum Technologies (2023)
  • The Expressive Power of Restricted Quantum Machine Learning Architectures
    • Invited talk, QHack (2023)
  • Contextuality for Quantum Advantage
    • Invited tutorial, Harvard University (2022)
  • Interpretable Quantum Advantage in Neural Sequence Learning
    • Invited talk, Masaryk University (2022)
  • Critical Points in Hamiltonian Agnostic Variational Quantum Algorithms
    • Invited talk, Quantum Research Seminars Toronto (2021)
  • Critical Points in Hamiltonian Agnostic Variational Quantum Algorithms
    • Invited talk, Centre for Quantum Technologies (2021)
  • Critical Points in Hamiltonian Agnostic Variational Quantum Algorithms
    • Invited talk, Quantum Algorithms and Applications seminar, Microsoft (2021)
  • Quantum Advantage in Basis-Enhanced Neural Sequence Models
    • Quantum Techniques in Machine Learning (QTML) (2021)
  • Near-Term Quantum-Classical Associative Adversarial Networks
    • Quantum Techniques in Machine Learning (QTML) (2020)
  • Quantum Machine Learning on NISQ Devices
    • Invited talk, Condensed Matter Seminar, Tufts University (2020)
  • Improved Training of Quantum Boltzmann Machines
    • American Physical Society March Meeting (2019)

    Notes

    Contextuality for Quantum Advantage - Invited tutorial, Harvard University
    MIT 8.311 Recitation Notes - Week 13
    MIT 8.311 Recitation Notes - Week 12
    MIT 8.311 Recitation Notes - Week 11
    MIT 8.311 Recitation Notes - Week 10
    MIT 8.311 Recitation Notes - Week 9
    MIT 8.311 Recitation Notes - Week 8
    MIT 8.311 Recitation Notes - Week 7
    MIT 8.311 Recitation Notes - Week 6
    MIT 8.311 Recitation Notes - Week 5
    MIT 8.311 Recitation Notes - Week 4
    MIT 8.311 Recitation Notes - Week 3
    MIT 8.311 Recitation Notes - Week 2
    MIT 8.311 Recitation Notes - Week 1