| 19 V | (University of Gdańsk) How to be a Copehagenistic-Qubistic Everettist? Gdzie: B-1-46 AND MS Teams [ZOA-test], 12:15Seminarium Zakładowe Online: [link] |
| 19 V | (IFT UJ) Local Thermal Operations and Classical Communication Gdzie: F-1-04 and ZOOM 14:15 Online: [link], pass: on request! Chaos i Informacja Kwantowa |
Figures 1 and 2 have no replication data, as they are purely graphical or are plots of mathematical formulas give in full in the article's main text. "Fig 34" directory contains replication data for figures 3 and 4. The description file is inside the directory
The files
The data has been exported and is provided as pickle files for easy loading. The only dependency required is numpy.
For the first file
import pickle import matplotlib.pyplot as pltwith open("pickled_Gamma_0_vs_Omega.bin", "rb") as f: omegas = pickle.load(f) gammas = pickle.load(f)
plt.plot(omegas, gammas[100]["lemon"]) plt.show()
Now "omegas" and "gammas" indicate values of $\Omega$ and $\Gamma$ as in the Figure.
The remaining two files are pickled dictionaries. After
tab1 = pickle.load(f) val1 = pickle.load(f) the tab1 contain labels for the dictionary "val1" holding the data. For example in the case of file "pickled_Gamma_Delta_trans.bin" is the Delta-dependence of the loss rate, so "tab1" contain Delta values ([-4000, -3000, -2000, -1000, 1000, 2000, 3000, 4000]). The dictionary data2[3000][100] (which indicate value of Delta=3000 and U/t=100) contains keys: 'modes', 'lemon', 'strip', and 'tot' keys that contain mean Gamma, for indicated mode numbers for the states that are quasi-free ('lemon'), quaisi-bound ('strip') or average over both ('tot').
The plots in the figure indicate sums over all the modes. Note that for high laying modes the data has been partially interpolated, and such data for every N=10 modes is provided.
| Fig34 | DIR | |
| pickled_Gamma_0_vs_Omega.bin | 4kB | |
| pickled_Gamma_Delta_trans.bin | 1MB | |
| pickled_Gamma_trans_vs_Omega.bin | 44kB |