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aaand now, let's try anti-zeno

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Valentin Boettcher 2022-07-11 18:23:17 +02:00
parent dec0705dae
commit 9908405f4b
2 changed files with 56 additions and 13 deletions
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5
python/energy_flow_proper/10_antizeno_engine/10_first_anti_zeno.py
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@ -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=.5,
ω_c=2,
ε_couple=0.2,
n=3,
@ -250,8 +250,9 @@ with aux.get_data(model) as data:
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).sum_baths(), ax=ax)
fs.plot_with_σ(model.t, model.total_energy_from_power(data), 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.system_energy(data), ax=ax)

64
python/energy_flow_proper/10_antizeno_engine/anti_zeno_engine.org
View file

@ -25,7 +25,10 @@ Init ray and silence stocproc.
#+end_src
#+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_17-56-08_601005_343893/sockets/plasma_store', 'raylet_socket_name': '/tmp/ray/session_2022-07-11_17-56-08_601005_343893/sockets/raylet', 'webui_url': '', 'session_dir': '/tmp/ray/session_2022-07-11_17-56-08_601005_343893', 'metrics_export_port': 33700, 'gcs_address': '141.30.17.225:52666', 'address': '141.30.17.225:52666', 'node_id': 'c868ef9cb4594a88f4d75d22b97e6e30132618843dcccc5031cf3e62'})
:RESULTS:
: 2022-07-11 18:20:21,473 INFO worker.py:956 -- Connecting to existing Ray cluster at address: 141.30.17.16:6379
: 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_2022-07-07_19-11-40_930040_398742/sockets/plasma_store.1', 'raylet_socket_name': '/tmp/ray/session_2022-07-07_19-11-40_930040_398742/sockets/raylet', 'webui_url': '', 'session_dir': '/tmp/ray/session_2022-07-07_19-11-40_930040_398742', 'metrics_export_port': 63895, 'gcs_address': '141.30.17.16:6379', 'address': '141.30.17.16:6379', 'node_id': 'ca50ebda6b5629030bd1ad7d4047a1a1fde48b5b2041bf7976b7d105'})
:END:
#+begin_src jupyter-python :results none
from hops.util.logging_setup import logging_setup
@ -199,7 +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=.5,
ω_c=2,
ε_couple=0.2,
n=3,
@ -245,8 +248,8 @@ Let's test the assumptions of the paper.
#+RESULTS:
:RESULTS:
| <matplotlib.lines.Line2D | at | 0x7f4c9b5ddfa0> |
[[file:./.ob-jupyter/f197c60a4dd01166b654064ca4d794d7645d96df.svg]]
| <matplotlib.lines.Line2D | at | 0x7f5192fc8c70> |
[[file:./.ob-jupyter/dd12f4cd01aa9f158355ddff8a1151fa1fb0107e.svg]]
:END:
#+begin_src jupyter-python :tangle nil
@ -264,8 +267,8 @@ Let's test the assumptions of the paper.
#+RESULTS:
:RESULTS:
| <matplotlib.lines.Line2D | at | 0x7f4c8cedb5b0> |
[[file:./.ob-jupyter/0b52df5f4c1b72a077f2a791adbb3c8c1bd2fe32.svg]]
| <matplotlib.lines.Line2D | at | 0x7f5192f4adc0> |
[[file:./.ob-jupyter/1c64ce4d6cddbe1bf47c0c9900650b58860bdbb7.svg]]
:END:
#+begin_src jupyter-python
@ -322,7 +325,33 @@ Let's test the assumptions of the paper.
#+end_src
#+RESULTS:
: 602a65a4-50ca-485c-84f9-0f0adcce05f1
#+begin_example
[INFO hops.core.integration 537693] Choosing the nonlinear integrator.
[INFO hops.core.integration 537693] Using 21 integrators.
[INFO hops.core.integration 537693] Some 100 trajectories have to be integrated.
[INFO hops.core.integration 537693] Using 1820 hierarchy states.
0% 0/100 [00:00<?, ?it/s](integration_task pid=15589, ip=141.30.17.8) WARNING:root:Using mpmath for the zeta (lerch phi) function. Install a recent version of ``arb`` for more performance.
(integration_task pid=15599, ip=141.30.17.8) WARNING:root:Using mpmath for the zeta (lerch phi) function. Install a recent version of ``arb`` for more performance.
(integration_task pid=15598, ip=141.30.17.8) WARNING:root:Using mpmath for the zeta (lerch phi) function. Install a recent version of ``arb`` for more performance.
(integration_task pid=15600, ip=141.30.17.8) WARNING:root:Using mpmath for the zeta (lerch phi) function. Install a recent version of ``arb`` for more performance.
(integration_task pid=15590, ip=141.30.17.8) WARNING:root:Using mpmath for the zeta (lerch phi) function. Install a recent version of ``arb`` for more performance.
(integration_task pid=15596, ip=141.30.17.8) WARNING:root:Using mpmath for the zeta (lerch phi) function. Install a recent version of ``arb`` for more performance.
(integration_task pid=15592, ip=141.30.17.8) WARNING:root:Using mpmath for the zeta (lerch phi) function. Install a recent version of ``arb`` for more performance.
(integration_task pid=15591, ip=141.30.17.8) WARNING:root:Using mpmath for the zeta (lerch phi) function. Install a recent version of ``arb`` for more performance.
(integration_task pid=15597, ip=141.30.17.8) WARNING:root:Using mpmath for the zeta (lerch phi) function. Install a recent version of ``arb`` for more performance.
(integration_task pid=15595, ip=141.30.17.8) WARNING:root:Using mpmath for the zeta (lerch phi) function. Install a recent version of ``arb`` for more performance.
(integration_task pid=15593, ip=141.30.17.8) WARNING:root:Using mpmath for the zeta (lerch phi) function. Install a recent version of ``arb`` for more performance.
(integration_task pid=15594, ip=141.30.17.8) WARNING:root:Using mpmath for the zeta (lerch phi) function. Install a recent version of ``arb`` for more performance.
(integration_task pid=454933, ip=141.30.17.16) WARNING:root:Using mpmath for the zeta (lerch phi) function. Install a recent version of ``arb`` for more performance.
(integration_task pid=454936, ip=141.30.17.16) WARNING:root:Using mpmath for the zeta (lerch phi) function. Install a recent version of ``arb`` for more performance.
(integration_task pid=454934, ip=141.30.17.16) WARNING:root:Using mpmath for the zeta (lerch phi) function. Install a recent version of ``arb`` for more performance.
(integration_task pid=454935, ip=141.30.17.16) WARNING:root:Using mpmath for the zeta (lerch phi) function. Install a recent version of ``arb`` for more performance.
(integration_task pid=454937, ip=141.30.17.16) WARNING:root:Using mpmath for the zeta (lerch phi) function. Install a recent version of ``arb`` for more performance.
(integration_task pid=454938, ip=141.30.17.16) WARNING:root:Using mpmath for the zeta (lerch phi) function. Install a recent version of ``arb`` for more performance.
(integration_task pid=454972, ip=141.30.17.16) WARNING:root:Using mpmath for the zeta (lerch phi) function. Install a recent version of ``arb`` for more performance.
(integration_task pid=454973, ip=141.30.17.16) WARNING:root:Using mpmath for the zeta (lerch phi) function. Install a recent version of ``arb`` for more performance.
13% 13/100 [03:12<21:29, 14.83s/it]
#+end_example
#+begin_src jupyter-python
@ -336,7 +365,7 @@ Let's test the assumptions of the paper.
#+end_src
#+RESULTS:
[[file:./.ob-jupyter/11e4a0abf6a0dea91c5542b16a38a0be24d727b7.svg]]
[[file:./.ob-jupyter/d85d02e49429ad1149d9173297b0fb0c54215487.svg]]
- **too fast decoupling kills it**
- no anti-zeno effects without detuning?
@ -401,8 +430,9 @@ We need the time points where we sample the total energy.
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).sum_baths(), ax=ax)
fs.plot_with_σ(model.t, model.total_energy_from_power(data), 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.system_energy(data), ax=ax)
@ -412,7 +442,13 @@ We need the time points where we sample the total energy.
#+end_src
#+RESULTS:
[[file:./.ob-jupyter/7da4d68c0d2592ebdf69ace2067f107f23b6593f.svg]]
: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:
@ -450,9 +486,15 @@ One cycle power.
#+RESULTS:
:RESULTS:
: -0.10798709005058302
[[file:./.ob-jupyter/833b64a5b95211ec1753bc3c4f064c5b53836f48.svg]]
:END:
: -0.03467681556531242
[[file:./.ob-jupyter/5bdb4e604aa258376721b1b1f98f6e1c7023e2e3.svg]]
:END: