The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Hamiltonian Learning and Certification Using Quantum Resources
Open accessAbstract
In recent years quantum simulation has made great strides, culminating in experiments that existing supercomputers cannot easily simulate. Although this raises the possibility that special purpose analog quantum simulators may be able to perform computational tasks that existing computers cannot, it also introduces a major challenge: certifying that the quantum simulator is in fact simulating the correct quantum dynamics. We provide an algorithm that, under relatively weak assumptions, can be used to efficiently infer the Hamiltonian of a large but untrusted quantum simulator using a trusted quantum simulator. We illustrate the power of this approach by showing numerically that it can inexpensively learn the Hamiltonians for large frustrated Ising models, demonstrating that quantum resources can make certifying analog quantum simulators tractable.
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Citations
Cites
Statistical distance and the geometry of quantum states
Braunstein, S. L., & Caves, C. M. (1994) · Physical Review Letters · doi:10.1103/physrevlett.72.3439 · Cited by 3,176
Lloyd, S. (1996) · Science · doi:10.1126/science.273.5278.1073 · Cited by 3,088 · cited 1×
“Quantum information processing promises to dramatically advance physics and chemistry by providing efficient simulators for the Schr¨odinger or Dirac equations [1–3].”
BackgroundBritton, J. W., Sawyer, B. C., Keith, A. C., Wang, C.-C. J., Freericks, J. K., Uys, H., Biercuk, M. J., & Bollinger, J. J. (2012) · Nature · doi:10.1038/nature10981 · Cited by 941
Quantum simulation of frustrated Ising spins with trapped ions
Kim, K., Chang, M.-S., Korenblit, S., Islam, R., Edwards, E. E., Freericks, J. K., Lin, G.-D., Duan, L.-M., & Monroe, C. (2010) · Nature · doi:10.1038/nature09071 · Cited by 750 · cited 1×
“Analog quantum simulation raises the possibility that special purpose analog devices may be able to address such problems using current or near–future hardware [4–6].”
BackgroundQuantum simulation of the Dirac equation
Gerritsma, R., Kirchmair, G., Zähringer, F., Solano, E., Blatt, R., & Roos, C. F. (2010) · Nature · doi:10.1038/nature08688 · Cited by 725 · cited 1×
“Quantum information processing promises to dramatically advance physics and chemistry by providing efficient simulators for the Schr¨odinger or Dirac equations [1–3].”
BackgroundUniversal Digital Quantum Simulation with Trapped Ions
Lanyon, B. P., Hempel, C., Nigg, D., Müller, M., Gerritsma, R., Zähringer, F., Schindler, P., Barreiro, J. T., Rambach, M., Kirchmair, G., et al. (2011) · Science · doi:10.1126/science.1208001 · Cited by 613
Can one trust quantum simulators?
Hauke, P., Cucchietti, F. M., Tagliacozzo, L., Deutsch, I., & Lewenstein, M. (2012) · Reports on Progress in Physics · doi:10.1088/0034-4885/75/8/082401 · Cited by 315 · cited 1×
“A major objection to this avenue of inquiry is that analog simulators are not necessarily trustworthy [7, 8] and certification of them is not known to be efficient.”
BackgroundEstimation of many-body quantum Hamiltonians via compressive sensing
Shabani, A., Mohseni, M., Lloyd, S., Kosut, R. L., & Rabitz, H. (2011) · Physical Review A · doi:10.1103/physreva.84.012107 · Cited by 78
Cited by
Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., & Lloyd, S. (2017) · Nature · doi:10.1038/nature23474 · Cited by 4,779
Self-citationElucidating reaction mechanisms on quantum computers
Reiher, M., Wiebe, N., Svore, K. M., Wecker, D., & Troyer, M. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1619152114 · Cited by 745
Self-citationHybrid Quantum-Classical Approach to Quantum Optimal Control
Li, J., Yang, X., Peng, X., & Sun, C.-P. (2017) · Physical Review Letters · doi:10.1103/physrevlett.118.150503 · Cited by 278
Accelerated Variational Quantum Eigensolver
Wang, D., Higgott, O., & Brierley, S. (2019) · Physical Review Letters · doi:10.1103/physrevlett.122.140504 · Cited by 241
Artificial intelligence and machine learning for quantum technologies
Krenn, M., Landgraf, J., Foesel, T., & Marquardt, F. (2023) · Physical Review A · doi:10.1103/physreva.107.010101 · Cited by 162
Metadata
- Authors
- Wiebe, N., Granade, C., Ferrie, C., & Cory, D. G.
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2014-05-14
- Volume
- 112
- Issue
- 19
- Pages
- 190501-190501
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W2064517571
- Cited by
- 217
- References
- 8
- FWCI
- 15.51
- Type
- journal article
- Language
- en
- Open access
- green
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- Wiebe, N., Granade, C., Ferrie, C., & Cory, D. G. (2014). Hamiltonian Learning and Certification Using Quantum Resources. Physical Review Letters. https://doi.org/10.1103/physrevlett.112.190501
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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