profile picture

Eric R. Anschuetz

Burke Fellow at Caltech

Education

Ph.D. in Physics

Massachusetts Institute of Technology
2017 - 2023

M.A. in Physics

Harvard University
2015 - 2017

B.A. in Physics and Mathematics

Harvard University
2013 - 2017

About

I’m a Burke Fellow at Caltech interested in both quantum spin glasses and machine learning. I’ve thought a lot about how to design provably efficient and expressive quantum machine learning models, showing they’re great at solving certain kinds of problems and not so great at solving even more kinds of problems. Studying when such models perform poorly has also led me to think about the hardness of approximation of typical instances of quantum spin glasses, both from a classical and quantum algorithmic perspective.

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Sometimes this page is out of date; check out my Google Scholar or my arXiv page to see what I’ve been up to recently!

Publications

  • Optimizing Sparse SYK
    • M. Ding, R. King, B.T. Kiani, E.R. Anschuetz
    arXiv:2506.09037 [quant-ph]
  • Efficient Learning Implies Quantum Glassiness
    • E.R. Anschuetz
    arXiv:2505.00087 [quant-ph]
  • Strongly interacting fermions are non-trivial yet non-glassy
    • E.R. Anschuetz, C.-F. Chen, B.T. Kiani, R. King
    arXiv:2408.15699 [quant-ph]
  • 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
    arXiv:2404.07231 [quant-ph]
  • Q-CHOP: Quantum constrained Hamiltonian optimization
    • M.A. Perlin, Ruslan Shaydulin, ..., E.R. Anschuetz, M. Pistoia, P. Gokhale
    arXiv:2403.05653 [quant-ph]
  • Arbitrary Polynomial Separations in Trainable Quantum Machine Learning
    • E.R. Anschuetz, X. Gao
    arXiv:2402.08606 [quant-ph]
  • Does provable absence of barren plateaus imply classical simulability? Or, why we need to rethink variational quantum computing
    • M. Cerezo, M. Larocca, D. García-Martín, ..., E.R. Anschuetz, Z. Holmes
    arXiv:2312.09121 [quant-ph]
  • 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
    • P. Gokhale, E.R. Anschuetz, C. Campbell et al.
    arXiv:2212.03850 [quant-ph]
  • 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
    2022 IEEE International Conference on Quantum Computing and Engineering (QCE) (2022)
  • 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

  • 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, 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 (2021)
  • Near-Term Quantum-Classical Associative Adversarial Networks
    • Quantum Techniques in Machine Learning (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)