publication . Article . Other literature type . Preprint . 2017

QInfer: statistical inference software for quantum applications

Christopher Granade; Christopher Ferrie; Ian Hincks; Steven Casagrande; Thomas Alexander; Jonathan Gross; Michal Kononenko; Yuval Sanders;
Open Access English
  • Published: 25 Apr 2017 Journal: Quantum (issn: 2521-327X, Copyright policy)
  • Publisher: Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften
Abstract
Characterizing quantum systems through experimental data is critical to applications as diverse as metrology and quantum computing. Analyzing this experimental data in a robust and reproducible manner is made challenging, however, by the lack of readily-available software for performing principled statistical analysis. We improve the robustness and reproducibility of characterization by introducing an open-source library, QInfer, to address this need. Our library makes it easy to analyze data from tomography, randomized benchmarking, and Hamiltonian learning experiments either in post-processing, or online as data is acquired. QInfer also provides functionality ...
Subjects
free text keywords: Physics, QC1-999, Quantum Physics, Physics - Data Analysis, Statistics and Probability, Statistics - Applications, Benchmarking, Metrology, Quantum technology, Software, business.industry, business, Data mining, computer.software_genre, computer, Mathematics, Experimental data, Machine learning, Statistical inference, Quantum computer, Artificial intelligence, Robustness (computer science), Discrete mathematics
Funded by
NSERC
Project
  • Funder: Natural Sciences and Engineering Research Council of Canada (NSERC)
,
ARC| Discovery Projects - Grant ID: DP160102426
Project
  • Funder: Australian Research Council (ARC) (ARC)
  • Project Code: DP160102426
  • Funding stream: Discovery Projects
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62 references, page 1 of 5

[1] G. M. D'Ariano, M. D. Laurentis, M. G. A. Paris, A. Porzio, and S. Solimeno, “Quantum tomography as a tool for the characterization of optical devices,” Journal of Optics B: Quantum and Semiclassical Optics 4, S127 (2002). [OpenAIRE]

[2] J. B. Altepeter, D. Branning, E. Jeffrey, T. C. Wei, P. G. Kwiat, R. T. Thew, J. L. O'Brien, M. A. Nielsen, and A. G. White, “Ancilla-assisted quantum process tomography,” Phys. Rev. Lett. 90, 193601 (2003). [OpenAIRE]

[3] J. J. Wallman and S. T. Flammia, “Randomized benchmarking with confidence,” New Journal of Physics 16, 103032 (2014). [OpenAIRE]

[4] C. Granade, C. Ferrie, and D. G. Cory, “Accelerated randomized benchmarking,” New Journal of Physics 17, 013042 (2015).

[5] A. S. Holevo, Statistical Structure of Quantum Theory, edited by R. Beig, J. Ehlers, U. Frisch, K. Hepp, W. Hillebrandt, D. Imboden, R. L. Jaffe, R. Kippenhahn, R. Lipowsky, H. v. L o¨hneysen, I. Ojima, H. A. Weidenmu¨ ller, J. Wess, J. Zittartz, and W. Beiglb o¨ck, Lecture Notes in Physics Monographs, Vol. 67 (Springer Berlin Heidelberg, Berlin, Heidelberg, 2001).

[6] C. W. Helstrom, Quantum Detection and Estimation Theory (Academic Press, 1976).

[7] B. Goldacre, “Scientists are hoarding data and it's ruining medical research,” BuzzFeed (2015).

[8] V. Stodden and S. Miguez, “Best Practices for Computational Science: Software Infrastructure and Environments for Reproducible and Extensible Research,” Journal of Open Research Software 2 (2014), 10.5334/jors.ay.

[9] J. P. A. Ioannidis, “Why Most Published Research Findings Are False,” PLOS Med 2, e124 (2005).

[10] R. Hoekstra, R. D. Morey, J. N. Rouder, and E.-J. Wagenmakers, “Robust misinterpretation of confidence intervals,” Psychonomic Bulletin & Review , 1 (2014).

[11] J. P. A. Ioannidis, “How to Make More Published Research True,” PLOS Med 11, e1001747 (2014).

[12] Y. R. Sanders, J. J. Wallman, and B. C. Sanders, “Bounding quantum gate error rate based on reported average fidelity,” New Journal of Physics 18, 012002 (2016). [OpenAIRE]

[13] F. Pe´rez and B. E. Granger, “IPython: A System for Interactive Scientific Computing,” Computing in Science and Engineering 9, 21 (2007).

[14] Jupyter Development Team, “Jupyter,” (2016).

[15] S. R. Piccolo and M. B. Frampton, “Tools and techniques for computational reproducibility,” GigaScience 5, 30 (2016); A. de Vries, “Using R with Jupyter Notebooks,” (2015); D. Donoho and V. Stodden, “Reproducible research in the mathematical sciences,” in The Princeton Companion to Applied Mathematics, edited by N. J. Higham (2015).

62 references, page 1 of 5
Abstract
Characterizing quantum systems through experimental data is critical to applications as diverse as metrology and quantum computing. Analyzing this experimental data in a robust and reproducible manner is made challenging, however, by the lack of readily-available software for performing principled statistical analysis. We improve the robustness and reproducibility of characterization by introducing an open-source library, QInfer, to address this need. Our library makes it easy to analyze data from tomography, randomized benchmarking, and Hamiltonian learning experiments either in post-processing, or online as data is acquired. QInfer also provides functionality ...
Subjects
free text keywords: Physics, QC1-999, Quantum Physics, Physics - Data Analysis, Statistics and Probability, Statistics - Applications, Benchmarking, Metrology, Quantum technology, Software, business.industry, business, Data mining, computer.software_genre, computer, Mathematics, Experimental data, Machine learning, Statistical inference, Quantum computer, Artificial intelligence, Robustness (computer science), Discrete mathematics
Funded by
NSERC
Project
  • Funder: Natural Sciences and Engineering Research Council of Canada (NSERC)
,
ARC| Discovery Projects - Grant ID: DP160102426
Project
  • Funder: Australian Research Council (ARC) (ARC)
  • Project Code: DP160102426
  • Funding stream: Discovery Projects
Download fromView all 4 versions
Quantum
Article . 2017
Quantum
Article . 2017
Provider: Crossref
Quantum
Article
Provider: UnpayWall
62 references, page 1 of 5

[1] G. M. D'Ariano, M. D. Laurentis, M. G. A. Paris, A. Porzio, and S. Solimeno, “Quantum tomography as a tool for the characterization of optical devices,” Journal of Optics B: Quantum and Semiclassical Optics 4, S127 (2002). [OpenAIRE]

[2] J. B. Altepeter, D. Branning, E. Jeffrey, T. C. Wei, P. G. Kwiat, R. T. Thew, J. L. O'Brien, M. A. Nielsen, and A. G. White, “Ancilla-assisted quantum process tomography,” Phys. Rev. Lett. 90, 193601 (2003). [OpenAIRE]

[3] J. J. Wallman and S. T. Flammia, “Randomized benchmarking with confidence,” New Journal of Physics 16, 103032 (2014). [OpenAIRE]

[4] C. Granade, C. Ferrie, and D. G. Cory, “Accelerated randomized benchmarking,” New Journal of Physics 17, 013042 (2015).

[5] A. S. Holevo, Statistical Structure of Quantum Theory, edited by R. Beig, J. Ehlers, U. Frisch, K. Hepp, W. Hillebrandt, D. Imboden, R. L. Jaffe, R. Kippenhahn, R. Lipowsky, H. v. L o¨hneysen, I. Ojima, H. A. Weidenmu¨ ller, J. Wess, J. Zittartz, and W. Beiglb o¨ck, Lecture Notes in Physics Monographs, Vol. 67 (Springer Berlin Heidelberg, Berlin, Heidelberg, 2001).

[6] C. W. Helstrom, Quantum Detection and Estimation Theory (Academic Press, 1976).

[7] B. Goldacre, “Scientists are hoarding data and it's ruining medical research,” BuzzFeed (2015).

[8] V. Stodden and S. Miguez, “Best Practices for Computational Science: Software Infrastructure and Environments for Reproducible and Extensible Research,” Journal of Open Research Software 2 (2014), 10.5334/jors.ay.

[9] J. P. A. Ioannidis, “Why Most Published Research Findings Are False,” PLOS Med 2, e124 (2005).

[10] R. Hoekstra, R. D. Morey, J. N. Rouder, and E.-J. Wagenmakers, “Robust misinterpretation of confidence intervals,” Psychonomic Bulletin & Review , 1 (2014).

[11] J. P. A. Ioannidis, “How to Make More Published Research True,” PLOS Med 11, e1001747 (2014).

[12] Y. R. Sanders, J. J. Wallman, and B. C. Sanders, “Bounding quantum gate error rate based on reported average fidelity,” New Journal of Physics 18, 012002 (2016). [OpenAIRE]

[13] F. Pe´rez and B. E. Granger, “IPython: A System for Interactive Scientific Computing,” Computing in Science and Engineering 9, 21 (2007).

[14] Jupyter Development Team, “Jupyter,” (2016).

[15] S. R. Piccolo and M. B. Frampton, “Tools and techniques for computational reproducibility,” GigaScience 5, 30 (2016); A. de Vries, “Using R with Jupyter Notebooks,” (2015); D. Donoho and V. Stodden, “Reproducible research in the mathematical sciences,” in The Princeton Companion to Applied Mathematics, edited by N. J. Higham (2015).

62 references, page 1 of 5
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