hiro/master-thesis
1
0
Fork 0
mirror of https://github.com/vale981/master-thesis synced 2025-03-05 18:11:42 -05:00
Code Issues Projects Releases Packages Wiki Activity Actions

anti zeno without cooldown 1000

Browse source
This commit is contained in:
Valentin Boettcher 2022-07-12 17:15:30 +02:00
parent be64212d1f
commit 69a042b47b
2 changed files with 14 additions and 13 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

2
python/energy_flow_proper/10_antizeno_engine/10_first_anti_zeno.py
View file

@ -260,7 +260,7 @@ with aux.get_data(model) as data:
#ax.plot(model.t, ut.smoothen(model.t, model.total_energy(data).value, frac=.1))
#ax.plot(model.t, np.gradient(model.total_energy(data).value))
ts_begin = τ_init + ((τ_c + τ_off) * np.arange(0, n) - Δ_switch)
ts_begin = τ_init + ((τ_c + τ_off) * np.arange(0, n-1) - Δ_switch)
ts_end = τ_c + ts_begin + 2 * Δ_switch
ind_begin = np.searchsorted(model.t, ts_begin)
ind_end = np.searchsorted(model.t, ts_end)

25
python/energy_flow_proper/10_antizeno_engine/anti_zeno_engine.org
View file

@ -248,8 +248,8 @@ Let's test the assumptions of the paper.
#+RESULTS:
:RESULTS:
| <matplotlib.lines.Line2D | at | 0x7f518ead6f70> |
[[file:./.ob-jupyter/9cf2dc28bc9a8a8123042f79d9702fa25b04f99a.svg]]
| <matplotlib.lines.Line2D | at | 0x7f518eaca9d0> |
[[file:./.ob-jupyter/724673c144d604c0d468da57794f75631fa59464.svg]]
:END:
#+begin_src jupyter-python :tangle nil
@ -443,7 +443,7 @@ Let's test the assumptions of the paper.
#+end_src
#+RESULTS:
[[file:./.ob-jupyter/381fba40c5134ab6ddf8744ef0c980fcf689c875.svg]]
[[file:./.ob-jupyter/db4cca12d9a50dc32fd834c080a4b36a1802d4a7.svg]]
- **too fast decoupling kills it**
- no anti-zeno effects without detuning?
@ -508,13 +508,7 @@ We need the time points where we sample the total energy.
#+end_src
#+RESULTS:
:RESULTS:
: Loading: 98% 49/50 [00:07<00:00, 6.37it/s]
: [INFO root 537693] Writing cache to: results/3b50a2bb478fafb22b3d2f6fd3f25200c1483b9fc544a4a96e70dc7984ce195f_b7cd558634733fdb6d35da1623675474d43c06c4b9a8b27b62b299138d6a643c_op_exp_task_100_None_1ff6d34b7de893995dfd0ae8efe2ed770a6773ef52b6e717b4b4ee9b9e5d285d.npy
: Loading: 98% 98/100 [00:20<00:00, 4.67it/s]
: [INFO root 537693] Writing cache to: results/interaction_b7cd558634733fdb6d35da1623675474d43c06c4b9a8b27b62b299138d6a643c_interaction_energy_task_100_None_a1ebae168ba6027d405a66e94ca963c84f8aedb16621da5954b5631bcd1636c4.npy
[[file:./.ob-jupyter/600931f9143f8f955aa7938fb5c31af0f1d81b83.svg]]
:END:
[[file:./.ob-jupyter/f8f7bc3f1b8fa44fe49bcce62f47ce418c3aca97.svg]]
@ -526,7 +520,7 @@ We need the time points where we sample the total energy.
One cycle power.
#+begin_src jupyter-python
ts_begin = τ_init + ((τ_c + τ_off) * np.arange(0, n) - Δ_switch)
ts_begin = τ_init + ((τ_c + τ_off) * np.arange(0, n-1) - Δ_switch)
ts_end = τ_c + ts_begin + 2 * Δ_switch
ind_begin = np.searchsorted(model.t, ts_begin)
ind_end = np.searchsorted(model.t, ts_end)
@ -552,9 +546,16 @@ One cycle power.
#+RESULTS:
:RESULTS:
: -0.0440125572928138
[[file:./.ob-jupyter/5dc7ccf0f740aa282c134ea13c897f79ccfb5de1.svg]]
:END:
: -0.10190789225469887
[[file:./.ob-jupyter/a731a44cf0b555b2b4bda0d0d99ea0e1ad57f2c1.svg]]
:END: