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try longer coupling + larger detune

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Valentin Boettcher 2022-07-11 14:43:28 +02:00
parent 114b585488
commit 3a1a399bd4
2 changed files with 53 additions and 38 deletions
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25
python/energy_flow_proper/10_antizeno_engine/10_first_anti_zeno.py
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@ -173,14 +173,17 @@ def anti_zeno_engine(
), ),
) )
model, Δ, (τ_mod, τ_c, τ_bath, cycles, model.ω_s, ω_0, τ_s, τ_off, n, Δ_switch, τ_init) = anti_zeno_engine( (
model,
Δ,
(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=5, Δ=5,
ε=1, ε=1,
#ε=.7,
ω_c=1, ω_c=1,
ε_couple=0.6, ε_couple=0.2,
n=6, n=6,
detune=-0.8, detune=-0.9,
ω_0=20, ω_0=20,
T_c=8, T_c=8,
T_h=40, T_h=40,
@ -188,10 +191,10 @@ model, Δ, (τ_mod, τ_c, τ_bath, cycles, model.ω_s, ω_0, τ_s, τ_off, n, Δ
γ=5 / 10, γ=5 / 10,
switch_cycles=1, switch_cycles=1,
therm_initial_state=False, therm_initial_state=False,
s=[1]*2, s=[1] * 2,
ε_init=.2, ε_init=0.2,
terms = 6, terms=6,
dt=.01 dt=0.01,
) )
model.k_max = 4 model.k_max = 4
# model, params = anti_zeno_engine(ε=1/2, ε_couple=1e-4, n=1, detune=.5, δ=[.1,.1]) # model, params = anti_zeno_engine(ε=1/2, ε_couple=1e-4, n=1, detune=.5, δ=[.1,.1])
@ -243,9 +246,9 @@ plt.plot(model.t, ut.smoothen(model.t, ρ_ee, frac=.06, it=0))
with aux.get_data(model) as data: with aux.get_data(model) as data:
_, ax = plt.subplots() _, ax = plt.subplots()
# fs.plot_with_σ(model.t, model.bath_energy(data), bath=0, ax=ax) fs.plot_with_σ(model.t, model.bath_energy(data), bath=0, ax=ax)
# fs.plot_with_σ(model.t, model.bath_energy(data), bath=1, ax=ax) fs.plot_with_σ(model.t, model.bath_energy(data), bath=1, ax=ax)
#fs.plot_with_σ(model.t, model.bath_energy(data).sum_baths(), ax=ax) fs.plot_with_σ(model.t, model.bath_energy(data).sum_baths(), ax=ax)
#fs.plot_with_σ(model.t, model.total_energy(data), ax=ax) #fs.plot_with_σ(model.t, model.total_energy(data), ax=ax)
#fs.plot_with_σ(model.t, model.interaction_energy(data).for_bath(1), ax=ax) #fs.plot_with_σ(model.t, model.interaction_energy(data).for_bath(1), ax=ax)

66
python/energy_flow_proper/10_antizeno_engine/anti_zeno_engine.org
View file

@ -25,7 +25,7 @@ Init ray and silence stocproc.
#+end_src #+end_src
#+RESULTS: #+RESULTS:
: RayContext(dashboard_url='', python_version='3.9.13', ray_version='1.13.0', ray_commit='e4ce38d001dbbe09cd21c497fedd03d692b2be3e', address_info={'node_ip_address': '141.30.17.225', 'raylet_ip_address': '141.30.17.225', 'redis_address': None, 'object_store_address': '/tmp/ray/session_2022-07-11_10-51-42_657442_829151/sockets/plasma_store', 'raylet_socket_name': '/tmp/ray/session_2022-07-11_10-51-42_657442_829151/sockets/raylet', 'webui_url': '', 'session_dir': '/tmp/ray/session_2022-07-11_10-51-42_657442_829151', 'metrics_export_port': 47462, 'gcs_address': '141.30.17.225:63734', 'address': '141.30.17.225:63734', 'node_id': 'fd4bb6cfe84298ee455eadce0266cbe6331c8bf2c71d91b2fa299752'}) : RayContext(dashboard_url='', python_version='3.9.13', ray_version='1.13.0', ray_commit='e4ce38d001dbbe09cd21c497fedd03d692b2be3e', address_info={'node_ip_address': '141.30.17.225', 'raylet_ip_address': '141.30.17.225', 'redis_address': None, 'object_store_address': '/tmp/ray/session_2022-07-11_13-58-20_276114_35102/sockets/plasma_store', 'raylet_socket_name': '/tmp/ray/session_2022-07-11_13-58-20_276114_35102/sockets/raylet', 'webui_url': '', 'session_dir': '/tmp/ray/session_2022-07-11_13-58-20_276114_35102', 'metrics_export_port': 63919, 'gcs_address': '141.30.17.225:60837', 'address': '141.30.17.225:60837', 'node_id': '282b42830d5ff26f04c6280c3cd20e672efbdb52867015623c35edde'})
#+begin_src jupyter-python :results none #+begin_src jupyter-python :results none
from hops.util.logging_setup import logging_setup from hops.util.logging_setup import logging_setup
@ -193,14 +193,17 @@ Init ray and silence stocproc.
* Model Definition * Model Definition
#+begin_src jupyter-python #+begin_src jupyter-python
model, Δ, (τ_mod, τ_c, τ_bath, cycles, model.ω_s, ω_0, τ_s, τ_off, n, Δ_switch, τ_init) = anti_zeno_engine( (
model,
Δ,
(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=5, Δ=5,
ε=1, ε=1,
#ε=.7,
ω_c=1, ω_c=1,
ε_couple=0.6, ε_couple=0.2,
n=6, n=6,
detune=-0.8, detune=-0.9,
ω_0=20, ω_0=20,
T_c=8, T_c=8,
T_h=40, T_h=40,
@ -208,10 +211,10 @@ Init ray and silence stocproc.
γ=5 / 10, γ=5 / 10,
switch_cycles=1, switch_cycles=1,
therm_initial_state=False, therm_initial_state=False,
s=[1]*2, s=[1] * 2,
ε_init=.2, ε_init=0.2,
terms = 6, terms=6,
dt=.01 dt=0.01,
) )
model.k_max = 4 model.k_max = 4
# model, params = anti_zeno_engine(ε=1/2, ε_couple=1e-4, n=1, detune=.5, δ=[.1,.1]) # model, params = anti_zeno_engine(ε=1/2, ε_couple=1e-4, n=1, detune=.5, δ=[.1,.1])
@ -226,7 +229,7 @@ Let's test the assumptions of the paper.
#+end_src #+end_src
#+RESULTS: #+RESULTS:
: 6 : 19
** BCFs and Modulations ** BCFs and Modulations
#+begin_src jupyter-python :tangle nil #+begin_src jupyter-python :tangle nil
@ -242,8 +245,8 @@ Let's test the assumptions of the paper.
#+RESULTS: #+RESULTS:
:RESULTS: :RESULTS:
| <matplotlib.lines.Line2D | at | 0x7f488b48df40> | | <matplotlib.lines.Line2D | at | 0x7fc87a521700> |
[[file:./.ob-jupyter/8723c6af359ceab18de1610520d92b9fdd02be5b.svg]] [[file:./.ob-jupyter/5ea0a35cd05cf2cf4c0126978e35a10d11416e28.svg]]
:END: :END:
#+begin_src jupyter-python :tangle nil #+begin_src jupyter-python :tangle nil
@ -256,13 +259,13 @@ Let's test the assumptions of the paper.
plt.plot(ωs, np.sinc((ωs - ω_0 - Δ) * τ_s * cycles)) plt.plot(ωs, np.sinc((ωs - ω_0 - Δ) * τ_s * cycles))
plt.plot(ωs, np.sinc((ωs - ω_0 - Δ) * τ_s * cycles * 2), color="orange", linewidth=.5) plt.plot(ωs, np.sinc((ωs - ω_0 - Δ) * τ_s * cycles * 2), color="orange", linewidth=.5)
plt.plot(ωs, np.sinc((ωs - ω_0 - Δ) * τ_s * cycles * 10), color="yellow", linewidth=.4) plt.plot(ωs, np.sinc((ωs - ω_0 - Δ) * τ_s * cycles * 10), color="yellow", linewidth=.4)
#plt.xlim(2, 4) plt.xlim(20, 30)
#+end_src #+end_src
#+RESULTS: #+RESULTS:
:RESULTS: :RESULTS:
| <matplotlib.lines.Line2D | at | 0x7fefdd8a7670> | | 20.0 | 30.0 |
[[file:./.ob-jupyter/89fa9d020cfafcb0ae5f7de1a41babd430954a82.svg]] [[file:./.ob-jupyter/3a1786b8d1a6a7908c454f193523beda1ce82a2e.svg]]
:END: :END:
#+begin_src jupyter-python #+begin_src jupyter-python
@ -343,8 +346,8 @@ Let's test the assumptions of the paper.
#+RESULTS: #+RESULTS:
:RESULTS: :RESULTS:
: <matplotlib.legend.Legend at 0x7f4899b689d0> : <matplotlib.legend.Legend at 0x7fc87c4a29a0>
[[file:./.ob-jupyter/9b1a1b35f9ce833fe41b19d8d50c18f6c85047fd.svg]] [[file:./.ob-jupyter/937986fe8e46f733a643430ae62eb572ff64fca6.svg]]
:END: :END:
- **too fast decoupling kills it** - **too fast decoupling kills it**
@ -382,7 +385,7 @@ Let's test the assumptions of the paper.
: pts[1:N+1, 1] = dep1slice : pts[1:N+1, 1] = dep1slice
: /nix/store/5s7kjvxclc9c53l6jzzp3c6fhbc52myg-python3-3.9.13-env/lib/python3.9/site-packages/matplotlib/axes/_axes.py:5224: ComplexWarning: Casting complex values to real discards the imaginary part : /nix/store/5s7kjvxclc9c53l6jzzp3c6fhbc52myg-python3-3.9.13-env/lib/python3.9/site-packages/matplotlib/axes/_axes.py:5224: ComplexWarning: Casting complex values to real discards the imaginary part
: pts[N+2:, 1] = dep2slice[::-1] : pts[N+2:, 1] = dep2slice[::-1]
[[file:./.ob-jupyter/d106ce5fe0fb377b978239e7e68b86a87ba225ab.svg]] [[file:./.ob-jupyter/cd0c69bb0ecbf66e8154d4fe2875f53655d72e29.svg]]
:END: :END:
- no steady state ... but we have to average... - no steady state ... but we have to average...
@ -395,8 +398,8 @@ Let's test the assumptions of the paper.
#+RESULTS: #+RESULTS:
:RESULTS: :RESULTS:
| <matplotlib.lines.Line2D | at | 0x7f488b950a30> | | <matplotlib.lines.Line2D | at | 0x7fc8905f7910> |
[[file:./.ob-jupyter/b055915031dec0963da129357ed8ce92b51d591d.svg]] [[file:./.ob-jupyter/1f66c12d5d63678125a0ba2775363698d48f3f74.svg]]
:END: :END:
** TODO Power and Efficiency ** TODO Power and Efficiency
@ -407,9 +410,9 @@ We need the time points where we sample the total energy.
#+begin_src jupyter-python #+begin_src jupyter-python
with aux.get_data(model) as data: with aux.get_data(model) as data:
_, ax = plt.subplots() _, ax = plt.subplots()
# fs.plot_with_σ(model.t, model.bath_energy(data), bath=0, ax=ax) fs.plot_with_σ(model.t, model.bath_energy(data), bath=0, ax=ax)
# fs.plot_with_σ(model.t, model.bath_energy(data), bath=1, ax=ax) fs.plot_with_σ(model.t, model.bath_energy(data), bath=1, ax=ax)
#fs.plot_with_σ(model.t, model.bath_energy(data).sum_baths(), ax=ax) fs.plot_with_σ(model.t, model.bath_energy(data).sum_baths(), ax=ax)
#fs.plot_with_σ(model.t, model.total_energy(data), ax=ax) #fs.plot_with_σ(model.t, model.total_energy(data), ax=ax)
#fs.plot_with_σ(model.t, model.interaction_energy(data).for_bath(1), ax=ax) #fs.plot_with_σ(model.t, model.interaction_energy(data).for_bath(1), ax=ax)
@ -421,7 +424,11 @@ We need the time points where we sample the total energy.
#+end_src #+end_src
#+RESULTS: #+RESULTS:
[[file:./.ob-jupyter/9d4baee52cd16a70d788c3351e96ffe5255858e8.svg]] [[file:./.ob-jupyter/7e5d23cb9e46290af7792e33bd244b6f476d990e.svg]]
[[file:./.ob-jupyter/0bf02827d9041de26d4e60b8f5af28ee068cc058.svg]]
@ -455,9 +462,14 @@ One cycle power.
#+RESULTS: #+RESULTS:
:RESULTS: :RESULTS:
: -0.03996888620839219
[[file:./.ob-jupyter/c1504df12b138f8ca873b863d4bf605aa127341b.svg]]
:END:
: -0.03951090584819158 : -0.03951090584819158
[[file:./.ob-jupyter/673437250abea43fbff9003efe88dafac1cd98a4.svg]] [[file:./.ob-jupyter/673437250abea43fbff9003efe88dafac1cd98a4.svg]]
:END:
@ -494,8 +506,8 @@ It is anti zeno, tested it with turning coupling on and off but no cooldown
#+RESULTS: #+RESULTS:
:RESULTS: :RESULTS:
: 6.41113197470654e-05 : 5.741482766795489e-05
[[file:./.ob-jupyter/1cf181c3eceb944bc365a27a5cd5f6363e1ce7b6.svg]] [[file:./.ob-jupyter/97d96e8931b5b208c905061d9e9ee7a15b401324.svg]]
:END: :END: