The DOI matches its Crossref record; 920 of the 920 references it cites were found in a registry or an index.
Photonic quantum metrology
Open accessAbstract
Quantum metrology is one of the most promising applications of quantum technologies. The aim of this research field is the estimation of unknown parameters exploiting quantum resources, whose application can lead to enhanced performances with respect to classical strategies. Several physical quantum systems can be employed to develop quantum sensors, and photonic systems represent ideal probes for a large number of metrological tasks. Here, the authors review the basic concepts behind quantum metrology and then focus on the application of photonic technology for this task, with particular attention to phase estimation. The authors describe the current state of the art in the field in terms of platforms and quantum resources. Furthermore, the authors present the research area of multiparameter quantum metrology, where multiple parameters have to be estimated at the same time. The authors conclude by discussing the current experimental and theoretical challenges and the open questions toward implementation of photonic quantum sensors with quantum-enhanced performances in the presence of noise.
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Cites
Can Quantum-Mechanical Description of Physical Reality Be Considered Complete?
Einstein, A., Podolsky, B., & Rosen, N. (1935) · Physical Review · doi:10.1103/physrev.47.777 · Cited by 16,654
A new optimizer using particle swarm theory
Eberhart, R., & Kennedy, J. (2002) · MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science · doi:10.1109/mhs.1995.494215 · Cited by 16,067
Observation of Gravitational Waves from a Binary Black Hole Merger
Abbott, B. P., Abbott, R., Abbott, T. D., Abernathy, M. R., Acernese, F., Ackley, K., Adams, C., Adams, T., Addesso, P., Adhikari, R. X., et al. (2016) · Physical Review Letters · doi:10.1103/physrevlett.116.061102 · Cited by 14,151
On the Einstein Podolsky Rosen paradox
Bell, J. S. (1964) · Physics Physique Fizika · doi:10.1103/physicsphysiquefizika.1.195 · Cited by 12,252
Orbital angular momentum of light and the transformation of Laguerre-Gaussian laser modes
Allen, L., Beijersbergen, M. W., Spreeuw, R. J. C., & Woerdman, J. P. (1992) · Physical Review A · doi:10.1103/physreva.45.8185 · Cited by 10,455
Horodecki, R., Horodecki, P., Horodecki, M., & Horodecki, K. (2009) · Reviews of Modern Physics · doi:10.1103/revmodphys.81.865 · Cited by 10,118
On the Quantum Correction For Thermodynamic Equilibrium
Wigner, E. (1932) · Physical Review · doi:10.1103/physrev.40.749 · Cited by 9,283
Coherence in Spontaneous Radiation Processes
Dicke, R. H. (1954) · Physical Review · doi:10.1103/physrev.93.99 · Cited by 7,445
A scheme for efficient quantum computation with linear optics
Knill, E., Laflamme, R., & Milburn, G. J. (2001) · Nature · doi:10.1038/35051009 · Cited by 5,880
Machine learning for molecular and materials science
Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., & Walsh, A. (2018) · Nature · doi:10.1038/s41586-018-0337-2 · Cited by 4,968
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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
Self-citationArtificial 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
Observation of the quantum Gouy phase
Hiekkamäki, M., Barros, R. F., Ornigotti, M., & Fickler, R. (2022) · Nature Photonics · doi:10.1038/s41566-022-01077-w · Cited by 33
Metadata
- Authors
- Polino, E., Valeri, M., Spagnolo, N., & Sciarrino, F.
- Venue
- AVS Quantum Science
- Publisher
- American Vacuum Society
- Published
- 2020-06-01
- Volume
- 2
- Issue
- 2
- ISSN
- 2639-0213 (ISSN-L)
- OpenAlex
- W3012104901
- Cited by
- 414
- References
- 920
- FWCI
- 21.69
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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