The Hamiltonian describing a quantum many-body system can be learned using measurements in thermal equilibrium. Now, a learning algorithm applicable to many natural systems has been found that requires exponentially fewer measurements than existing methods.
This is a preview of subscription content, log in via an institution to check access.
References
Haah, J., Kothari, R. & Tang, E. Nat. Phys. https://doi.org/10.1038/s41567-023-02376-x (2024).
Anshu, A. et al. Nat. Phys. 17, 931–935 (2021).
Santhanam, N. P. & Wainwright, M. J. IEEE Trans. Inform. Theor. 58, 4117–4134 (2012).
Kuwahara, T. & Saito, K. Ann. Phys. 421, 168278 (2020).
Bresler, G. ACM Symp. Theor. Comput. 47, 771–782 (2015).
Hamilton, L., Koehler, F. & Moitra, A. Adv. Neural Inform. Process. Syst. 30, 2460–2469 (2017).
Klivans, A. R. & Meka, R. IEEE Symp. Found. Comp. Sci. 58, 343–354 (2017).
Vuffray, M. et al. Adv. Neural Inform. Process. Syst. 29, 2603–2611 (2016).
Bakshi, A. et al. Preprint at https://arxiv.org/abs/2310.02243 (2023).
Anshu, A. & Arunachalam, S. Nat. Rev. Phys. 6, 59–69 (2024).
Chen, S. et al. IEEE Symp. Found. Comp. Sci. 62, 574–585 (2021).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The author declares no competing interests.
Rights and permissions
About this article
Cite this article
Chen, S. Efficient learning of many-body systems. Nat. Phys. (2024). https://doi.org/10.1038/s41567-024-02393-4
Published:
DOI: https://doi.org/10.1038/s41567-024-02393-4
- Springer Nature Limited