The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Neural-network quantum state tomography
Open accessAbstract
The experimental realization of increasingly complex synthetic quantum systems calls for the development of general theoretical methods to validate and fully exploit quantum resources. Quantum state tomography (QST) aims to reconstruct the full quantum state from simple measurements, and therefore provides a key tool to obtain reliable analytics1–3. However, exact brute-force approaches to QST place a high demand on computational resources, making them unfeasible for anything except small systems4,5. Here we show how machine learning techniques can be used to perform QST of highly entangled states with more than a hundred qubits, to a high degree of accuracy. We demonstrate that machine learning allows one to reconstruct traditionally challenging many-body quantities—such as the entanglement entropy—from simple, experimentally accessible measurements. This approach can benefit existing and future generations of devices ranging from quantum computers to ultracold-atom quantum simulators6–8. Unsupervised machine learning techniques can efficiently perform quantum state tomography of large, highly entangled states with high accuracy, and allow the reconstruction of many-body quantities from simple experimentally accessible measurements.
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Cites
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Self-citationQuantum annealing with manufactured spins
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Cited by
Noisy intermediate-scale quantum algorithms
Bharti, K., Cervera-Lierta, A., Kyaw, T. H., Haug, T., Alperin-Lea, S., Anand, A., Degroote, M., Heimonen, H., Kottmann, J. S., Menke, T., et al. (2022) · Reviews of Modern Physics · doi:10.1103/revmodphys.94.015004 · Cited by 2,231
Predicting many properties of a quantum system from very few measurements
Huang, H.-Y., Kueng, R., & Preskill, J. (2020) · Nature Physics · doi:10.1038/s41567-020-0932-7 · Cited by 1,240
Discovering Physical Concepts with Neural Networks
Iten, R., Metger, T., Wilming, H., del Rio, L., & Renner, R. (2020) · Physical Review Letters · doi:10.1103/physrevlett.124.010508 · Cited by 458
The randomized measurement toolbox
Elben, A., Flammia, S. T., Huang, H.-Y., Kueng, R., Preskill, J., Vermersch, B., & Zoller, P. (2022) · Nature Reviews Physics · doi:10.1038/s42254-022-00535-2 · Cited by 355
Reconstructing quantum states with generative models
Carrasquilla, J., Torlai, G., Melko, R. G., & Aolita, L. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0028-1 · Cited by 317
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Metadata
- Authors
- Torlai, G., Mazzola, G., Carrasquilla, J., Troyer, M., Melko, R., & Carleo, G.
- Venue
- Nature Physics
- Published
- 2018-02-26
- Volume
- 14
- Issue
- 5
- Pages
- 447-450
- ISSN
- 1745-2473 (ISSN-L)
- 1745-2481
- OpenAlex
- W2782726627
- Cited by
- 843
- References
- 32
- FWCI
- 43.93
- Type
- journal article
- Language
- en
- Open access
- green
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- Torlai, G., Mazzola, G., Carrasquilla, J., Troyer, M., Melko, R., & Carleo, G. (2018). Neural-network quantum state tomography. Nature Physics. https://doi.org/10.1038/s41567-018-0048-5
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