The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Experimental quantum Hamiltonian learning
Open accessAbstract
With the help of a quantum simulator and Bayesian inference it is possible to determine the unknown Hamiltonian of a quantum system. An experiment demonstrates this using a photonic quantum simulator and a solid-state system. The efficient characterization of quantum systems1,2,3, the verification of the operations of quantum devices4,5,6 and the validation of underpinning physical models7,8,9, are central challenges for quantum technologies10,11,12 and fundamental physics13,14. The computational cost of such studies could be improved by machine learning enhanced by quantum simulators15,16. Here we interface two different quantum systems through a classical channel—a silicon-photonics quantum simulator and an electron spin in a diamond nitrogen–vacancy centre—and use the former to learn the Hamiltonian of the latter via Bayesian inference. We learn the salient Hamiltonian parameter with an uncertainty of approximately 10−5. Furthermore, an observed saturation in the learning algorithm suggests deficiencies in the underlying Hamiltonian model, which we exploit to further improve the model. We implement an interactive version of the protocol and experimentally show its ability to characterize the operation of the quantum photonic device.
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Cites
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Self-citationScalable Quantum Simulation of Molecular Energies
O’Malley, P. J. J., Babbush, R., Kivlichan, I. D., Romero, J., McClean, J. R., Barends, R., Kelly, J., Roushan, P., Tranter, A., Ding, N., et al. (2016) · Physical Review X · doi:10.1103/physrevx.6.031007 · Cited by 1,059
Towards quantum chemistry on a quantum computer
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Cited by
Integrated photonic quantum technologies
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Self-citationMultidimensional quantum entanglement with large-scale integrated optics
Wang, J., Paesani, S., Ding, Y., Santagati, R., Skrzypczyk, P., Salavrakos, A., Tura, J., Augusiak, R., Mančinska, L., Bacco, D., et al. (2018) · Science · doi:10.1126/science.aar7053 · Cited by 816
Self-citationChallenges and opportunities in quantum machine learning
Cerezo, M., Verdon, G., Huang, H.-Y., Cincio, L., & Coles, P. J. (2022) · Nature Computational Science · doi:10.1038/s43588-022-00311-3 · Cited by 689
Photonic quantum information processing: a concise review
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The potential and global outlook of integrated photonics for quantum technologies
Pelucchi, E., Fagas, G., Aharonovich, I., Englund, D., Figueroa, E., Gong, Q., Hannes, H., Liu, J., Lu, C.-Y., Matsuda, N., et al. (2021) · Nature Reviews Physics · doi:10.1038/s42254-021-00398-z · Cited by 533
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Metadata
- Authors
- Wang, J., Paesani, S., Santagati, R., Knauer, S., Gentile, A. A., Wiebe, N., Petruzzella, M., O’Brien, J. L., Rarity, J. G., Laing, A., & Thompson, M. G.
- Venue
- Nature Physics
- Published
- 2017-03-13
- Volume
- 13
- Issue
- 6
- Pages
- 551-555
- ISSN
- 1745-2473 (ISSN-L)
- 1745-2481
- OpenAlex
- W2597208492
- Cited by
- 247
- References
- 17
- FWCI
- 12.46
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Wang, J., Paesani, S., Santagati, R., Knauer, S., Gentile, A. A., Wiebe, N., Petruzzella, M., O’Brien, J. L., Rarity, J. G., Laing, A., & Thompson, M. G. (2017). Experimental quantum Hamiltonian learning. Nature Physics. https://doi.org/10.1038/nphys4074
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