Summary of Research
I am broadly interested in the intersection of quantum information and theoretical computer science, particularly quantum algorithms and learning theory. Previously, I have worked on provably-efficient classical ML algorithms for learning ground states, tomography of quantum states and processes, and quantum algorithms that outperform classical ones for certain learning tasks. I am also interested in proofs of quantumness and the connections of learning with cryptography.
Publications
L. Lewis, D. Gilboa, J. R. McClean. (2025). Quantum advantage for learning shallow neural networks with natural data distributions. arXiv preprint arXiv:2503.20879. Accepted to the 20th Theory of Quantum Computation, Communication, and Cryptography Conference (TQC 2025).
M. Wanner, L. Lewis, C. Bhattacharyya, D. Dubhashi, A. Gheorghiu. (2024). Predicting ground state properties: Constant sample complexity and deep learning algorithms. Advances of Neural Information Processing Systems 37, 33962-34024 (NeurIPS 2024).
J. Huang, L. Lewis, H.-Y. Huang, J. Preskill. (2024). Predicting adaptively chosen observables in quantum systems. arXiv preprint arXiv:2410.15501. Under review.
C. Wadhwa, L. Lewis, E. Kashefi, M. Doosti. (2024). Agnostic process tomography. arXiv preprint arXiv:2410.11957 Under review.
H. Zhao, L. Lewis (co-first author), I. Kannan, Y. Quek, H.-Y. Huang, M. C. Caro. (2024). Learning quantum states and unitaries of bounded gate complexity. PRX Quantum, 5(4), 040306 (On the Cover).
L. Lewis, H.-Y. Huang, V. T. Tran, S. Lehner, R. Kueng, J. Preskill. (2024). Improved machine learning algorithm for predicting ground state properties. Nature Communications, 15(1), 895.
L. Lewis, D. Zhu, A. Gheorghiu, C. Noel, O. Katz, B. Harraz, Q. Wang, A. Risinger, L. Feng, D. Biswas, L. Egan, T. Vidick, M. Cetina, C. Monroe. (2024). Experimental implementation of an efficient test of quantumness. Physical Review A, 109(1), 012610 (Editor's Suggestion).
D. Zhu, G. D. Kahanamoku-Meyer, L. Lewis, C. Noel, O. Katz, B. Harraz, Q. Wang, A. Risinger, L. Feng, D. Biswas, L. Egan, A. Gheorghiu, Y. Nam, T. Vidick, U. Vazirani, N. Y. Yao, M. Cetina, C. Monroe. (2023). Interactive proofs of quantumness using mid-circuit measurements. Nature Physics, 19(11), 1725-1731.
V. T. Tran, L. Lewis, H.-Y. Huang, J. Kofler, R. Kueng, S. Hochreiter, S. Lehner. (2022). Using shadows to learn ground state properties of quantum hamiltonians. Machine Learning and Physical Sciences Workshop at the 36th Conference on Neural Information Processing Systems (NeurIPS 2022).
Awards
Marshall Scholarship (2023)
NSF Graduate Research Fellowship Program (GRFP) (2023, declined)
Caltech Bhansali Family Prize for Outstanding Research in Computer Science (2023)
Barry M. Goldwater Scholarship (2022)
Caltech Herbert J. Ryser Memorial Scholarship in Mathematics (2022)
Mellon Mays Undergraduate Fellowship (2021-2023)