LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Knee, George C. (2017)
Publisher: IOP Publishing
Languages: English
Types: Preprint
Subjects: QC, Physics - History and Philosophy of Physics, Physics - Computational Physics, Quantum Physics
The Barrett--Cavalcanti--Lal--Maroney (BCLM) argument stands as the most effective means of demonstrating the reality of the quantum state. Its advantages include being derived from very few assumptions, and a robustness to experimental error. Finding the best way to implement the argument experimentally is an open problem, however, and involves cleverly choosing sets of states and measurements. I show that techniques from convex optimisation theory can be leveraged to numerically search for these sets, which then form a recipe for experiments that allow for the strongest statements about the ontology of the wavefunction to be made. The optimisation approach presented is versatile, efficient and can take account of the finite errors present in any real experiment. I find significantly improved low-cardinality sets which are guaranteed partially optimal for a BCLM test in low Hilbert space dimension. I further show that mixed states can be more optimal than pure states.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] Harrigan N and Spekkens R 2010 Found. Phys. 40 125
    • [2] Spekkens R W 2007 Phys. Rev. A 75 032110
    • [3] Leifer M 2014 Quanta 3 67-155
    • [4] Ringbauer M et al 2015 Nat. Phys. 11 249
    • [5] Bell J 1966 Rev. Mod. Phys. 38 447
    • [6] Bell J S 1964 Physics 1 195
    • [7] Clauser J F, Horne M A, Shimony A and Holt R A 1969 Phys. Rev. Lett. 23 880
    • [8] Hensen B et al 2015 Nature 526 682
    • [9] Giustina M et al 2015 Phys. Rev. Lett. 115 250401
    • [10] Shalm L K et al 2015 Phys. Rev. Lett. 115 250402
    • [11] Pusey M F, Barrett J and Rudolph T 2012 Nat. Phys. 8 475
    • [12] Kochen S and Specker E 1967 J. Math. Mech. 17 59
    • [13] Lewis P G, Jennings D, Barrett J and Rudolph T 2012 Phys. Rev. Lett. 109 150404
    • [14] Aaronson S, Bouland A, Chua L and Lowther G 2013 Phys. Rev. A 88 032111
    • [15] Nigg D et al 2016 New J. Phys. 18 013007
    • [16] Patra M K, Pironio S and Massar S 2013 Phys. Rev. Lett. 111 090402
    • [17] Colbeck R and Renner R 2012 Phys. Rev. Lett. 108 150402
    • [18] Hardy L 2013 Int. J. Mod. Phys. B 27 1345012
    • [19] Maroney O J E 2012 arXiv:1207.6906v2
    • [20] Barrett J, Cavalcanti E G, Lal R and Maroney O J E 2014 Phys. Rev. Lett. 112 250403
    • [21] Fuchs C A 1996 Distinguishability and accessible information in quantum theory PhD Thesis University of New Mexico arXiv:quantph/9601020v1
    • [22] Nielsen M and Chuang I 2004 Quantum Computation and Quantum Information (Cambridge Series on Information and the Natural Sciences) 1st ed (Cambridge: Cambridge University Press) (https://doi.org/10.1080/00107514.2011.587535)
    • [23] Harrigan N and Rudolph T 2007 arXiv:0709.4266v1
    • [24] Ballentine L 2014 arXiv:1402.5689v1
    • [25] Bandyopadhyay S, Jain R, Oppenheim J and Perry C 2014 Phys. Rev. A 89 022336
    • [26] Caves C M, Fuchs C A and Schack R 2002 Phys. Rev. A 66 062111
    • [27] Leifer M S 2014 Phys. Rev. Lett. 112 160404
    • [28] Branciard C 2014 Phys. Rev. Lett. 113 020409
    • [29] Hedemann S R 2013 arXiv:1303.5904v1
    • [30] Enkhbat R, Bazarsad Y and Enkhbayar J 2011 Int. J. Pure Appl. Math. 73 93
    • [31] Gorski J, Pfeuffer F and Klamroth K 2007 Math. Methods Oper. Res. 66 373
    • [32] Jeflea A 2003 An. St. Univ. Ovidius Constanta 11 87 (https://eudml.org/doc/125842)
    • [33] Grant M and Boyd S 2014 CVX: Matlab software for disciplined convex programming, version 2.1 (http://cvxr.com/cvx) October 2016
    • [34] Dutta A, Pawłowski M and Żukowski M 2015 Phys. Rev. A 91 042125
    • [35] Spekkens R W 2005 Phys. Rev. A 71 052108
    • [36] Leifer M S and Maroney O J E 2013 Phys. Rev. Lett. 110 120401
    • [37] Floudas C 2013 Deterministic Global Optimization: Theory, Methods and Applications (Nonconvex Optimization and Its Applications) (New York: Springer) (https://doi.org/10.1007/978-1-4757-4949-6)
    • [38] Cirel'son B S 1980 Lett. Math. Phys. 4 93
    • [39] Dinkelbach W 1967 Manage. Sci. 13 492
  • No related research data.
  • Discovered through pilot similarity algorithms. Send us your feedback.

Share - Bookmark

Cite this article