Hsin-Yuan Huang (Robert)

Hsin-Yuan Huang (Robert)

A theorist wondering how machines could discover new physics in our quantum universe

About

I am Hsin-Yuan Huang (黃信元, pronounced “Shin Yuan Huan”). I also go by the name Robert. I am currently a Senior Research Scientist at Google Quantum AI and a Visiting Scientist at MIT. In 2025, I will join Caltech as an Assistant Professor of Theoretical Physics.

I received my Ph.D. under the guidance of John Preskill and Thomas Vidick. My doctoral dissertation, titled Learning in the Quantum Universe, was honored with the Milton and Francis Clauser Doctoral Prize — an award conferred annually to a single doctoral dissertation across all disciplines at Caltech that demonstrates the highest degree of originality and potential for opening up new avenues of human thought and endeavor.

Research Interest

My research aims to build a rigorous foundation for understanding how scientists, machines, and future quantum computers can learn and discover new phenomena governing our quantum-mechanical universe (molecules, materials, pharmaceutics, exotic quantum matter, engineered quantum devices, etc.).

I leverage quantum information theory, quantum many-body physics, learning theory, and complexity theory to formalize and explore new questions in the following directions:

  • When can quantum machines learn and predict better than classical machines?
  • How to accelerate/automate the development of quantum and physical sciences?
  • What physical phenomena can classical vs quantum machines learn and discover?

My ultimate goal is to build quantum machines capable of discovering new facets of our universe beyond the capabilities of humans and classical machines.

Cartoon depiction of intelligence

Publications

Google Scholar provides a full list under chronological/citations order. Selected publications are labeled by :dart:.

  1. Predicting quantum channels over general product distributions
    (alphabetical order) S. Chen, J. D. Pont, J.-T. Hsieh, H.-Y. Huang, J. Lange, J. Li.
    arXiv (2024). [PDF]

  2. Classically estimating observables of noiseless quantum circuits
    A. Angrisani, A. Schmidhuber, M. S. Rudolph, M. Cerezo, Z. Holmes, H.-Y. Huang.
    arXiv (2024). [PDF]

  3. Quantum error correction below the surface code threshold
    (alphabetical order) Google Quantum AI and Collaborators.
    arXiv (2024). [PDF]

  4. :dart: Random unitaries in extremely low depth
    T. Schuster, J. Haferkamp, H.-Y. Huang.
    arXiv (2024). [PDF] [Thread on X]

  5. Tight bounds on Pauli channel learning without entanglement
    S. Chen, C. Oh, S. Zhou, H.-Y. Huang, L. Jiang.
    Physical Review Letters (2024). [PDF]

  6. :dart: Certifying almost all quantum states with few single-qubit measurements
    (alphabetical order) H.-Y. Huang, J. Preskill, M. Soleimanifar.
    FOCS (2024), Contributed talk at QIP (2024).
    [PDF] [Thread on X] [GitHub Code] [Invited talk at IBM]

  7. Learning conservation laws in unknown quantum dynamics
    Y. Zhan, A. Elben, H.-Y. Huang, Y. Tong.
    PRX Quantum (2024). [PDF]

  8. :dart: Learning shallow quantum circuits
    H.-Y. Huang$\dagger$ (co-first author), Y. Liu$\dagger$, M. Broughton, I. Kim, A. Anshu, Z. Landau, J. R. McClean.
    STOC (2024), Short plenary talk at QIP (2024).
    Invited to SIAM Journal of Computing Special Issue.
    [PDF] [QIP Slide] [Thread on X] [PennyLane Demo]

  9. Improved machine learning algorithm for predicting ground state properties
    L. Lewis, H.-Y. Huang, V. T. Tran, S. Lehner, R. Kueng, J. Preskill.
    Nature Communications (2024), Contributed talk at QIP (2023).
    [PDF] [GitHub Code] [Thread on X] [Quanta Magazine]

  10. Entanglement-enabled advantage for learning a bosonic random displacement channel
    C. Oh, S. Chen, Y. Wong, S. Zhou, H.-Y. Huang, J. A.H. Nielsen, Z.-H. Liu, J. S. Neergaard-Nielsen, U. L. Andersen, L. Jiang, J. Preskill.
    arXiv (2024). [PDF]

  11. :dart: Local minima in quantum systems
    (alphabetical order) C.-F. Chen, H.-Y. Huang, J. Preskill, L. Zhou.
    STOC (2024), Contributed talk at QIP (2024).
    [PDF] [Thread on X] [Quanta Magazine]

  12. Learning quantum states and unitaries of bounded gate complexity
    H. Zhao, L. Lewis, I. Kannan, Y. Quek, H.-Y. Huang, M. C. Caro.
    arXiv (2023). [PDF]

  13. On quantum backpropagation, information reuse, and cheating measurement collapse
    A. Abbas, R. King, H.-Y. Huang, W. J Huggins, R. Movassagh, D. Gilboa, J. R McClean NeurIPS 2023 Spotlight. [PDF]

  14. Learning to predict arbitrary quantum processes
    H.-Y. Huang, S. Chen, J. Preskill.
    PRX Quantum (2023), Contributed talk at QIP (2023).
    [PDF] [GitHub Code] [Thread on X]

  15. The complexity of NISQ
    (alphabetical order) S. Chen, J. Cotler, H.-Y. Huang, J. Li.
    Nature Communications (2023), Contributed talk at QIP (2023). [PDF]

  16. Out-of-distribution generalization for learning quantum dynamics
    M. C. Caro$\dagger$, H.-Y. Huang$\dagger$ (co-first author), N. Ezzell, J. Gibbs, A. T. Sornborger, L. Cincio, P. J. Coles, Z. Holmes.
    Nature Communications (2023). [PDF] [News]

  17. Dynamical simulation via quantum machine learning with provable generalization
    J. Gibbs, Z. Holmes, M. C. Caro, N. Ezzell, H.-Y. Huang, L. Cincio, A. T. Sornborger, P. J. Coles. Physical Review Research (2024). [PDF]

  18. :dart: Learning many-body Hamiltonians with Heisenberg-limited scaling
    H.-Y. Huang$\dagger$ (co-first author), Y. Tong$\dagger$, Di Fang, Yuan Su.
    Physical Review Letters (2023), Short plenary talk at QIP (2023). [PDF] [Thread on X]

  19. The power and limitations of learning quantum dynamics incoherently
    S. Jerbi, J. Gibbs, M. S. Rudolph, M. C. Caro, P. J. Coles, H.-Y. Huang, Z. Holmes.
    arXiv (2023). [PDF]

  20. Preparing random states and benchmarking with many-body quantum chaos
    J. Choi, A. L. Shaw, I. S. Madjarov, X. Xie, R. Finkelstein, J. P. Covey, J. S. Cotler, D. K. Mark, H.-Y. Huang, A. Kale, H. Pichler, F. G. S. L. Brandao, S. Choi, M. Endres.
    Nature (2023). [PDF]

  21. Emergent quantum state designs from individual many-body wavefunctions
    J. Cotler$\dagger$, D. Mark$\dagger$, H.-Y. Huang$\dagger$ (co-first author), F. Hernandez, J. Choi, A. L. Shaw, M. Endres, S. Choi.
    PRX Quantum (2023). [PDF]

  22. The randomized measurement toolbox
    (alphabetical order) A. Elben, S. Flammia, H.-Y. Huang, R. Kueng, J. Preskill, B. Vermersch, P. Zoller.
    Nature Review Physics (2022). [PDF] [Tutorial at QIP 2022]

  23. Hardware-efficient learning of quantum many-body states
    K. V. Kirk, J. Cotler, H.-Y. Huang, M. D. Lukin.
    arXiv (2022). [PDF]

  24. :dart: Provably efficient machine learning for quantum many-body problems
    H.-Y. Huang, R. Kueng, G. Torlai, V. V. Albert, J. Preskill.
    Science (2022), Plenary talk at QIP (2022).
    [PDF] [GitHub Code] [Thread on X] [Caltech News] [Phys.org News] [IEEE Spectrum]
    [PennyLane Tutorial] [Invited talk at Simons Institute] [Quanta Magazine]

  25. Challenges and opportunities in quantum machine learning
    M. Cerezo, G. Verdon, H.Y. Huang, L. Cincio, P. Coles.
    Nature Computational Science (2022). [PDF]

  26. Generalization in quantum machine learning from few training data
    M. C. Caro, H.-Y. Huang, M. Cerezo, K. Sharma, A. Sornborger, L. Cincio, P. J. Coles.
    Nature Communications (2022).
    [PDF] [Los Alamos News] [PennyLane Tutorial]

  27. :dart: Quantum advantage in learning from experiments
    H.-Y. Huang, M. Broughton, J. Cotler, S. Chen, J. Li, M. Mohseni, H. Neven, R. Babbush, R. Kueng, J. Preskill, J. R. McClean.
    Science (2022).
    [PDF] [Thread on X] [arsTECHNICA News] [ScienceNews] [Phys.org News] [WIRED News] [NewScientist News] [Google AI blog] [PennyLane Tutorial] [Invited talk at IBM] [Nature News]

  28. Foundations for learning from noisy quantum experiments
    H.-Y. Huang, S. Flammia, J. Preskill.
    Contributed talk at QIP (2022). [PDF]

  29. Learning quantum states from their classical shadows
    H.-Y. Huang.
    Nature Review Physics (2022). [PDF] [Quanta Magazine]

  30. Exponential separation between learning with and without quantum memory
    (alphabetical order) S. Chen, J. Cotler, H.-Y. Huang, J. Li.
    FOCS (2021), Contributed talk at QIP (2022),
    Invited to SIAM Journal of Computing Special Issue. [PDF]

  31. Revisiting dequantization and quantum advantage in learning tasks
    (alphabetical order) J. Cotler, H.-Y. Huang, J. R. McClean.
    arXiv (2021). [PDF]

  32. A hierarchy for replica quantum advantage
    (alphabetical order) S. Chen, J. Cotler, H.-Y. Huang, J. Li.
    arXiv (2021). [PDF]

  33. What the foundations of quantum computer science teach us about chemistry.
    J. R. McClean, N. C. Rubin, J. Lee, M. P. Harrigan, T. E. O’Brien, R. Babbush, W. J. Huggins, H.-Y. Huang.
    Journal of Chemical Physics (2021). [PDF] [Talk at Simons Institute]

  34. Efficient estimation of Pauli observables by derandomization
    H.-Y. Huang, R. Kueng, J. Preskill.
    Physical Review Letters (2021), TQC (2021). [PDF] [GitHub Code]

  35. :dart: Power of data in quantum machine learning
    H.-Y. Huang, M. Broughton, M. Mohseni, R. Babbush, S. Boixo, H. Neven, J. R. McClean.
    Nature Communications (Featured), Contributed talk at QIP (2021).
    [PDF] [Talk at QIP] [Google AI blog] [TensorFlow blog] [TensorFlow Quantum Tutorial]

  36. :dart: Information-theoretic bounds on quantum advantage in machine learning
    H.-Y. Huang, R. Kueng, J. Preskill.
    Physical Review Letters (Editor’s Suggestion), Contributed talk at QIP (2021).
    [PDF] [Talk at QIP] [IQIM blog]

  37. Nearly-tight Trotterization of interacting electrons
    Y. Su, H.-Y. Huang, E. Campbell.
    Quantum (2021), Contributed talk at QIP (2021). [PDF] [Talk at QIP]

  38. Concentration for random product formulas
    C.-F. Chen$\dagger$, H.-Y. Huang$\dagger$ (co-first author), R. Kueng, J. Tropp.
    PRX Quantum (2021), TQC (2021). [PDF] [Talk at TQC]

  39. Near-term quantum algorithms for linear systems of equations
    H.-Y. Huang, K. Bharti, P. Rebentrost.
    New Journal of Physics (2021). [PDF]

  40. TensorFlow Quantum: A Software Framework for Quantum Machine Learning
    M. Broughton, G. Verdon, T. McCourt, A. J. Martinez, J. H. Yoo, S. V. Isakov, P. Massey, R. Halavati, M. Y. Niu, A. Zlokapa, E. Peters, O. Lockwood, A. Skolik, S. Jerbi, V. Dunjko, M. Leib, M. Streif, D. V. Dollen, H. Chen, S. Cao, R. Wiersema, H.-Y. Huang, J. R. McClean, R. Babbush, S. Boixo, D. Bacon, A. K. Ho, H. Neven, M. Mohseni.
    arXiv (2020). [PDF] [TFQ Website]

  41. Mixed-state entanglement from local randomized measurements
    A. Elben, R. Kueng, H.-Y. Huang, R. van Bijnen, C. Kokail, M. Dalmonte, P. Calabrese, B. Kraus, J. Preskill, P. Zoller, B. Vermersch.
    Physical Review Letters (2020). [PDF]

  42. :dart: Predicting many properties in a quantum system from very few measurements
    H.-Y. Huang, R. Kueng, J. Preskill.
    Nature Physics (2020), Short plenary talk at QIP (2020).
    [PDF] [GitHub Code] [Wikipedia page] [PennyLane Tutorial] [Talk by John Preskill] [Phys.org News] [Quanta Magazine]

  43. FlowQA: grasping flow in history for conversational machine comprehension
    H.-Y. Huang, E. Choi, W. Yih.
    ICLR (2019). [PDF]

  44. FusionNet: Fusing via Fully-aware attention with application to machine comprehension
    H.-Y. Huang, C. Zhu, Y. Shen, W. Chen.
    ICLR (2018). [PDF]

  45. A unified algorithm for one-class structured matrix factorization with side information
    H.-F. Yu, H.-Y. Huang, I. S. Dhillon, C.-J. Lin.
    AAAI (2017). [PDF]

  46. Linear and kernel classification: When to use which?
    H.-Y. Huang, C.-J. Lin.
    SDM (2016). [PDF]

  47. Dissecting the human protein-protein interaction network via phylogenetic decomposition
    C.-Y. Chen, A. Ho, H.-Y. Huang, H.-F. Juan and H.-C. Huang.
    Scientific Reports (2014).