About

I am Hsin-Yuan Huang (pronounced as “Shin Yuan Huan”, 黃信元), a Ph.D. student at Caltech advised by John Preskill and Thomas Vidick. I also go by the name Robert.

Research Interest:

My research aims to build a rigorous foundation for modeling how scientists, machines, and future quantum computers learn and make predictions about our quantum-mechanical universe (molecules, materials, pharmaceutics, exotic quantum matter, engineered quantum devices, etc) and discover new algorithmic tools to enhance one’s learning ability.

I utilize tools in quantum information theory, statistical learning theory, computational complexity theory, and quantum many-body physics to study questions such as:

  • How to efficiently learn and make predictions about complex quantum systems?
  • Could quantum AI learn faster and predict more accurately than classical AI?
  • How can machine learning advance quantum technology and physical sciences?

My ultimate dream is to build quantum machines capable of learning and discovering new facets of our universe beyond 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. :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] [Twitter thread] [Caltech News] [Phys.org News] [IEEE Spectrum]
    [PennyLane Tutorial] [Invited talk at Simons Institute]

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

  3. 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]

  4. :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] [Twitter thread] [arsTECHNICA News] [ScienceNews] [Phys.org News] [WIRED News] [NewScientist News] [Google AI blog] [PennyLane Tutorial] [Invited talk at IBM]

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

  6. 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.
    arXiv (2022). [pdf]

  7. 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]

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

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

  10. 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]

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

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

  13. 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]

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

  15. :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]

  16. :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]

  17. 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.
    arXiv (2021). [pdf]

  18. Emergent Randomness and Benchmarking from Many-Body Quantum Chaos
    J. Choi, A. Shaw, I. Madjarov, X. Xie, J. Covey, J. Cotler, D. Mark, H.-Y. Huang, A. Kale, H. Pichler, F. Brandao, S. Choi, M. Endres.
    arXiv (2021). [pdf]

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

  20. 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]

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

  22. 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]

  23. 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]

  24. :dart: Predicting many properties in a quantum system from very few measurements
    H.-Y. Huang, R. Kueng, J. Preskill.
    Nature Physics (2020), Single-track talk at QIP (2020).
    [pdf] [Wikipedia page] [PennyLane Tutorial] [Talk by John Preskill] [Phys.org News]


» 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).