Prediction through Description
Quantum Physics
Publications
2023
L. Lewis, H.-Y. Huang, V. T. Tran, S. Lehner, R. Kueng, and J. Preskill (2023) Improved Machine Learning Algorithm for Predicting Ground State Properties. arXiv:2301.13169, 2023-01-30. (more) (download)
2022
V. T. Tran, L. Lewis, H.-Y. Huang, J. Kofler, R. Kueng, S. Hochreiter, and S. Lehner (2022) Using Shadows to Learn Ground State Properties of Quantum Hamiltonians. Machine Learning and the Physical Sciences at NeurIPS 2022, 2022-12-03. (more) (download)
2021
T. Adler, M. Erhard, M. Krenn, J. Brandstetter, J. Kofler, and S. Hochreiter (2021) Quantum Optical Experiments Modeled by Long Short-Term Memory. Photonics, 8, 12, 535, 2021-11-26. (more) (download)
D. Flam-Shepherd, T. Wu, X. Gu, A. Cervera-Lierta, M. Krenn, and A. Aspuru-Guzik (2021) Learning Interpretable Representations of Entanglement in Quantum Optics Experiments using Deep Generative Models. arXiv:2109.02490, 2021-09-06. (more) (download)
M. Krenn, J. Kottmann, N. Tischler, and A. Aspuru-Guzik (2021) Conceptual Understanding through Efficient Automated Design of Quantum Optical Experiments. Physical Review X, 11, 3, 031044, 2021-08-26. (more) (download)
2019
T. Adler, M. Erhard, M. Krenn, J. Brandstetter, J. Kofler, and S. Hochreiter (2019) LSTM-Designed Quantum Experiments. Machine Learning and the Physical Sciences Workshop at NeurIPS 2019, Vancouver, 10-12 Dec 2019. (more) (download)