I am Hsin-Yuan Huang (pronounced as “Shin Yuan Huan”, 黃信元), a visiting scientist at MIT and a research scientist at Google Quantum AI. I received my Ph.D. in 2023 advised by John Preskill and Thomas Vidick. I also go by the name Robert.

In 2025, I will start as an Assistant Professor of Theoretical Physics at Caltech.

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 utilize concepts and tools in quantum information theory, quantum many-body physics, learning theory, and complexity theory to formalize and explore new questions spawning from 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 dream is to build quantum machines that can discover new facets of our universe beyond the capabilities of humans and classical machines.

Cartoon depiction of intelligence


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

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

  2. 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).
    [pdf] [QIP Slide] [Thread on X] [PennyLane Demo]

  3. 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]

  4. Learning conservation laws in unknown quantum dynamics
    Y. Zhan, A. Elben, Hsin-Yuan Huang, Y. Tong.
    PRX Quantum (2024). [pdf]

  5. 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]

  6. 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]

  7. 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]

  8. 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] [code] [Thread on X] [Quanta Magazine]

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

  10. Tight bounds on Pauli channel learning without entanglement
    S. Chen, C. Oh, S. Zhou, H.-Y. Huang, L. Jiang.
    arXiv (2023). [pdf]

  11. 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]

  12. 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]

  13. 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]

  14. 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]

  15. 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]

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

  17. 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]

  18. Learning to predict arbitrary quantum processes
    H.-Y. Huang, S. Chen, J. Preskill.
    Contributed talk at QIP (2023). [pdf] [code] [Thread on X]

  19. :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] [code] [Thread on X] [Caltech News] [Phys.org News] [IEEE Spectrum]
    [PennyLane Tutorial] [Invited talk at Simons Institute] [Quanta Magazine]

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

  21. 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]

  22. :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]

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

  24. 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.
    arXiv (2022). [pdf]

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

  26. 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]

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

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

  29. 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]

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

  31. :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]

  32. :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]

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

  34. 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]

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

  36. 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]

  37. 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]

  38. :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] [code] [Wikipedia page] [PennyLane Tutorial] [Talk by John Preskill] [Phys.org News] [Quanta Magazine]

» Pre-quantum

I have previously worked on machine learning at Chih-Jen Lin’s group, deep learning at Microsoft Research and Allen Institute of AI, and biology at Hsueh-Fen Juan’s lab.

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

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

  3. 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]

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

  5. 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).