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now: no cooldown

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Valentin Boettcher 2022-07-12 16:12:53 +02:00
parent 83a56aa33f
commit be64212d1f
2 changed files with 8 additions and 86 deletions
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22
python/energy_flow_proper/10_antizeno_engine/10_first_anti_zeno.py
View file

@ -179,7 +179,7 @@ def anti_zeno_engine(
(τ_mod, τ_c, τ_bath, cycles, model.ω_s, ω_0, τ_s, τ_off, n, Δ_switch, τ_init),
) = anti_zeno_engine(
Δ=11,
ε=.1,
ε=1,
ω_c=2,
ε_couple=0.5,
n=6,
@ -283,26 +283,6 @@ powers = (tot_e[ind_end] - tot_e[ind_begin]) / (model.t[ind_end[0]] - model.t[in
(powers[2:]).mean()
with aux.get_data(model) as data:
_, ax = plt.subplots()
#fs.plot_with_σ(model.t, model.bath_energy(data), bath=1, ax=ax)
fs.plot_diff_vs_sigma(model.t, model.total_energy(data, every=1000)[:-1], model.total_energy(data, every=1000)[-1], ax=ax)
ax.legend()
mean_norm = np.zeros_like(model.t)
with aux.get_data(model) as data:
for i in range(min(data.samples, 2)):
aux_state = data.aux_states[i, :]
# plt.plot(model.t, np.linalg.norm(aux_state, axis=1))
mean_norm += np.linalg.norm(aux_state, axis=1)
mean_norm /= data.samples
plt.plot(model.t, mean_norm / mean_norm.max())
plt.yscale("log")
mean_norm.max()
model, _ = anti_zeno_engine(Δ=5, γ=0.1 / 2, ω_c=1, ω_0=2, ε=1e-1, ε_couple=1 / 3, n=6, cycle_scale=1, switch_cycles=3)
_, ax = fs.plot_energy_overview(model, markersize=1)

72
python/energy_flow_proper/10_antizeno_engine/anti_zeno_engine.org
View file

@ -202,7 +202,7 @@ Init ray and silence stocproc.
(τ_mod, τ_c, τ_bath, cycles, model.ω_s, ω_0, τ_s, τ_off, n, Δ_switch, τ_init),
) = anti_zeno_engine(
Δ=11,
ε=.1,
ε=1,
ω_c=2,
ε_couple=0.5,
n=6,
@ -248,8 +248,8 @@ Let's test the assumptions of the paper.
#+RESULTS:
:RESULTS:
| <matplotlib.lines.Line2D | at | 0x7f519129d190> |
[[file:./.ob-jupyter/bd8dd45c519728f3c2034c29c3683783266b287f.svg]]
| <matplotlib.lines.Line2D | at | 0x7f518ead6f70> |
[[file:./.ob-jupyter/9cf2dc28bc9a8a8123042f79d9702fa25b04f99a.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/4f9c64f7f8eb5b4c06d7f920b6c43c17260d0122.svg]]
[[file:./.ob-jupyter/381fba40c5134ab6ddf8744ef0c980fcf689c875.svg]]
- **too fast decoupling kills it**
- no anti-zeno effects without detuning?
@ -552,70 +552,12 @@ One cycle power.
#+RESULTS:
:RESULTS:
: -0.05407415408283555
[[file:./.ob-jupyter/85cfe9187eafaf87bd20fea4b9a164f3fe6c2e44.svg]]
[[file:./.ob-jupyter/401b41048b1b2afe539bfddb6780a8a2e5ee4480.svg]]
: -0.10798709005058302
[[file:./.ob-jupyter/833b64a5b95211ec1753bc3c4f064c5b53836f48.svg]]
: -0.03467681556531242
[[file:./.ob-jupyter/5bdb4e604aa258376721b1b1f98f6e1c7023e2e3.svg]]
: -0.03951090584819158
[[file:./.ob-jupyter/673437250abea43fbff9003efe88dafac1cd98a4.svg]]
: -0.00032248844096049805
[[file:./.ob-jupyter/9ac6d0cae41eda15950b000878165c8e21547539.svg]]
Slightly better than constant coupling. But due to on-off or anti-zeno?
It is anti zeno, tested it with turning coupling on and off but no cooldown
#+begin_src jupyter-python
with aux.get_data(model) as data:
_, ax = plt.subplots()
#fs.plot_with_σ(model.t, model.bath_energy(data), bath=1, ax=ax)
fs.plot_diff_vs_sigma(model.t, model.total_energy(data, every=1000)[:-1], model.total_energy(data, every=1000)[-1], ax=ax)
ax.legend()
#+end_src
#+begin_src jupyter-python
mean_norm = np.zeros_like(model.t)
with aux.get_data(model) as data:
for i in range(min(data.samples, 2)):
aux_state = data.aux_states[i, :]
# plt.plot(model.t, np.linalg.norm(aux_state, axis=1))
mean_norm += np.linalg.norm(aux_state, axis=1)
mean_norm /= data.samples
plt.plot(model.t, mean_norm / mean_norm.max())
plt.yscale("log")
mean_norm.max()
#+end_src
#+RESULTS:
:RESULTS:
: 0.0005854498090217866
[[file:./.ob-jupyter/fad8f6abf218b6736cacbb9c1fe37ca825cf26d8.svg]]
: -0.10190789225469887
[[file:./.ob-jupyter/a731a44cf0b555b2b4bda0d0d99ea0e1ad57f2c1.svg]]
:END:
* Too long modulation?, more likely to small spectral sep or to strong coupling
- definitely not too long mod
#+begin_src jupyter-python :results none