Southwest Quantum Information and Technology

Data analysis for "A strong loophole-free test of local realism"

Presenting Author: Scott Glancy, (NIST, Boulder)
Contributing Author(s): Evan Meyer-Scott, Bradley G. Christensen, Peter Bierhorst, Michael A. Wayne, Martin J. Stevens, Thomas Gerrits, Scott Glancy, Deny R. Hamel, Michael S. Allman, Kevin J. Coakley, Shellee D. Dyer, Carson Hodge, Adriana E. Lita, Varun B. Verma, Camilla Lambrocco, Edward Tortorici, Alan L. Migdall, Yanbao Zhang, Daniel R. Kumor, William H. Farr, Francesco Marsili, Matthew D. Shaw, Jeffrey A. Stern, Carlos Abellan, Waldimar Amaya, Valerio Pruneri, Thomas Jennewein, Morgan W. Mitchell, Paul G. Kwiat, Joshua C. Bienfang, Richard P. Mirin, Emanuel Knill, Sae Woo Nam

Recent loophole-free tests of local realism have incorporated new analysis techniques to compute p-values (measures of statistical significance of the experiments). The new techniques do not require the support of assumptions upon which old techniques rely, they are effective for small data sets, and they accommodate imperfections in random number generators used to make measurement choices. In this talk I will describe the data analysis techniques used in the test of local realism performed at NIST. I will review the theory used to compute p-values, explain how it was implemented on our experiment's data, and compare our techniques to those used in the tests of local realism performed in Delft and Vienna.

Read this article online: http://arxiv.org/abs/1511.03189

(Session 2 : Thursday from 11:30 am - 12:00 pm)