Data from: Topological transitions of the generalized Pancharatnam-Berry phase
Data files
Nov 09, 2023 version files 22.81 KB
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README.md
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w0_0.6mm.txt
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w0_1.2mm.txt
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w0_1.7mm.txt
Abstract
Distinct from the dynamical phase, in a cyclic evolution, a system’s state may acquire an additional component, a.k.a. geometric phase. Recently, it has been demonstrated that geometric phases can be induced by a sequence of generalized measurements implemented on a single qubit. Furthermore, it has been predicted that such geometric phases may exhibit a topological transition as a function of the measurement strength. We demonstrate and study this transition experimentally by employing an optical platform where the qubit is represented by the polarisation of light and the weak measurement is performed by means of coupling with the spatial degree of freedom. Our protocol can be interpreted in terms of environment-induced geometric phases, whose values are topologically determined by the environment-system coupling strength. Our results show that the two limits of geometric phase induced by either sequences of either weak or projective measurements are topologically distinct.
README: Detected interference intensities
https://doi.org/10.5061/dryad.41ns1rnmg
Description of the data and file structure
The uploaded data are the raw interference intensities detected in the experiment.
Each file contains the dataset necessary to reproduce fig 3 b (one file for each panel). The file name specifies the waist value (also indicated in the figure panel).
Data can be imported in Mathematica with the code string “Import[“w0_0.6mm.txt ”,”Data”]”. A Matrixplot will show a 20x40 matrix where the axis with 20 values corresponds to delta and the axis with 40 values to alpha.
The geometric phase is obtained fitting, for each alpha, a function of delta of the form “A Cos(delta+phi)”, where the amplitude A and the geometric phase phi are fit parameters. The results and corresponding errors will yield the results in fig 3b
Code/Software
The GeneticAlgorithm.nb can be run on Mathematica13 and performs the genetic algorithm used to find the system imperfection parameters that better explain the data