The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Pattern Recognition Techniques for Boson Sampling Validation
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The continuous-time quantum walk has been adopted to identify signature of bosonic coalescence, to simulate Fano resonance, to shed light on the interplay between quantum coherence and noise for assist transport processes in complex networks, and to quantum fast hitting on hexagonal graphs.
“The continuous-time QW has been adopted to identify signature of bosonic coalescence [107], to simulate Fano resonance [108], to shed light on the interplay between quantum coherence and noise for assist transport processes in complex networks [40, 41, 109] ( Fig.5d ), and to quantum fast hitting on hexagonal graphs [110].”
From Integrated photonic quantum technologies (2020) · cites this paper as [109]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer
Shor, P. W. (1997) · SIAM Journal on Computing · doi:10.1137/s0097539795293172 · Cited by 8,472
Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., & Lloyd, S. (2017) · Nature · doi:10.1038/nature23474 · Cited by 4,779
Self-citationMeasurement of subpicosecond time intervals between two photons by interference
Hong, C. K., Ou, Z. Y., & Mandel, L. (1987) · Physical Review Letters · doi:10.1103/physrevlett.59.2044 · Cited by 4,369
Lloyd, S. (1996) · Science · doi:10.1126/science.273.5278.1073 · Cited by 3,088
The complexity of computing the permanent
Valiant, L. G. (1979) · Theoretical Computer Science · doi:10.1016/0304-3975(79)90044-6 · Cited by 2,705
An introduction to quantum machine learning
Schuld, M., Sinayskiy, I., & Petruccione, F. (2015) · Contemporary Physics · doi:10.1080/00107514.2014.964942 · Cited by 1,381
Carolan, J., Harrold, C., Sparrow, C., Martín-López, E., Russell, N. J., Silverstone, J. W., Shadbolt, P. J., Matsuda, N., Oguma, M., Itoh, M., et al. (2015) · Science · doi:10.1126/science.aab3642 · Cited by 1,093
The computational complexity of linear optics
Aaronson, S., & Arkhipov, A. (2011) · Proceedings of the forty-third annual ACM symposium on Theory of computing · doi:10.1145/1993636.1993682 · Cited by 874
Integrated multimode interferometers with arbitrary designs for photonic boson sampling
Crespi, A., Osellame, R., Ramponi, R., Brod, D. J., Galvão, E. F., Spagnolo, N., Vitelli, C., Maiorino, E., Mataloni, P., & Sciarrino, F. (2013) · Nature Photonics · doi:10.1038/nphoton.2013.112 · Cited by 662
Self-citationPhotonic Boson Sampling in a Tunable Circuit
Broome, M. A., Fedrizzi, A., Rahimi-Keshari, S., Dove, J., Aaronson, S., Ralph, T. C., & White, A. G. (2013) · Science · doi:10.1126/science.1231440 · Cited by 634
Experimental validation of photonic boson sampling
Spagnolo, N., Vitelli, C., Bentivegna, M., Brod, D. J., Crespi, A., Flamini, F., Giacomini, S., Milani, G., Ramponi, R., Mataloni, P., et al. (2014) · Nature Photonics · doi:10.1038/nphoton.2014.135 · Cited by 312
Self-citationAchieving quantum supremacy with sparse and noisy commuting quantum computations
Bremner, M. J., Montanaro, A., & Shepherd, D. J. (2017) · Quantum · doi:10.22331/q-2017-04-25-8 · Cited by 191
Flagged first, then by citations
Research works printed but not linked: 45
- 10.1007/BF02650179 · DOI · Paper page
- 10.1103/PhysRevLett.117.080501 · DOI · Paper page
- 10.1103/PhysRevLett.118.040502 · DOI
- 10.1103/PhysRevX.8.021010 · DOI
- 10.1038/s41567-018-0124-x · DOI · Paper page
- S. Aaronson, 2011, Proceedings of the Forty-third Annual ACM Symposium on Theory of Computing
- 10.1126/science.1231692 · DOI · Paper page
- 10.1038/nphoton.2013.102 · DOI · Paper page
- 10.1103/PhysRevLett.111.130503 · DOI
- 10.1038/nphoton.2014.152 · DOI
- 10.1103/PhysRevLett.113.100502 · DOI
- 10.1126/sciadv.1400255 · DOI · Paper page
- 10.1103/PhysRevLett.118.130503 · DOI · Paper page
- 10.1103/PhysRevLett.118.190501 · DOI
- 10.1038/nphoton.2017.63 · DOI
- 10.1103/PhysRevLett.120.230502 · DOI
- 10.1103/PhysRevLett.112.050504 · DOI
- S. Aaronson, 2014, Quantum Inf. Comput., 14, 1383, 10.26421/QIC14.15-16-7 · DOI
- N. Wiebe, 2014, Bull. EATCS, 112, 1
- 10.1038/ncomms9498 · DOI · Paper page
- 10.1103/PhysRevLett.118.153603 · DOI
- 10.1142/S021974991560028X · DOI · Paper page
- 10.1016/j.scib.2018.10.009 · DOI · Paper page
- 10.1103/PhysRevLett.116.123601 · DOI
- 10.1088/1367-2630/18/3/032001 · DOI
- 10.1088/1367-2630/18/4/041001 · DOI
- 10.1364/JOSAB.33.001835 · DOI
- 10.1038/s41566-018-0097-4 · DOI · Paper page
- 10.1088/2058-9565/aa540c · DOI · Paper page
- 10.1103/PhysRevLett.120.240404 · DOI · Paper page
- 10.1088/1367-2630/aaad92 · DOI
- 10.1103/PhysRevLett.113.020502 · DOI · Paper page
- 10.1103/PhysRevA.91.013811 · DOI
- 10.1038/ncomms10469 · DOI
- P. Simon, 2013, Too Big to Ignore: The Business Case for Big Data
- C. M. Bishop, 2006, Pattern Recognition and Machine Learning
- K. P. Murphy, 2012, Machine Learning: A Probabilistic Perspective
- L. Rokach, 2005, Data Mining and Knowledge Discovery Handbook
- J. MacQueen, 1967, Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability
- E. W. Forgy, 1965, Biometrics, 21, 768
- 10.1109/TIT.1982.1056489 · DOI · Paper page
- P. Clifford, 2018, SODA ’18: Proceedings of the 29th ACM-SIAM Symposium on Discrete Algorithms
- 10.1038/nphoton.2008.47 · DOI
- 10.1038/ncomms2616 · DOI · Paper page
- 10.1103/PhysRevA.93.012335 · DOI · Paper page
Cited by
Sorted by citations
Quantum computational advantage using photons
Zhong, H.-S., Wang, H., Deng, Y.-H., Chen, M.-C., Peng, L.-C., Luo, Y.-H., Qin, J., Wu, D., Ding, X., Hu, Y., et al. (2020) · Science · doi:10.1126/science.abe8770 · Cited by 2,405
Integrated photonic quantum technologies
Wang, J., Sciarrino, F., Laing, A., & Thompson, M. G. (2020) · Nature Photonics · doi:10.1038/s41566-019-0532-1 · Cited by 1,576
Self-citationPhotonic quantum information processing: a review
Flamini, F., Spagnolo, N., & Sciarrino, F. (2019) · Reports on Progress in Physics · doi:10.1088/1361-6633/aad5b2 · Cited by 770
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
Photonic implementation of boson sampling: a review
Brod, D. J., Galvão, E. F., Crespi, A., Osellame, R., Spagnolo, N., & Sciarrino, F. (2019) · Advanced Photonics · doi:10.1117/1.ap.1.3.034001 · Cited by 159
Self-citationReconfigurable continuously-coupled 3D photonic circuit for Boson Sampling experiments
Hoch, F., Piacentini, S., Giordani, T., Tian, Z.-N., Iuliano, M., Esposito, C., Camillini, A., Carvacho, G., Ceccarelli, F., Spagnolo, N., et al. (2022) · npj Quantum Information · doi:10.1038/s41534-022-00568-6 · Cited by 51
Self-citation
Metadata
- Authors
- Agresti, I., Viggianiello, N., Flamini, F., Spagnolo, N., Crespi, A., Osellame, R., Wiebe, N., & Sciarrino, F.
- Venue
- Physical Review X
- Publisher
- American Physical Society (APS)
- Published
- 2019-01-23
- Volume
- 9
- Issue
- 1
- ISSN
- 2160-3308
- Cited by
- 50
- References
- 12
- Language
- en
- Funders
- H2020 European Research Council (307783, 742745)
- Horizon 2020 Framework Programme (641039)
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review