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and another exploratory

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Valentin Boettcher 2022-06-24 13:18:03 +02:00
parent 24a623a697
commit 57dfd963f9
2 changed files with 30 additions and 26 deletions
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7
python/energy_flow_proper/10_antizeno_engine/10_first_anti_zeno.py
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@ -158,7 +158,8 @@ model, Δ, (τ_mod, τ_c, τ_bath, cycles, model.ω_s, ω_0, τ_s, τ_off, n, Δ
switch_cycles=1, switch_cycles=1,
therm_initial_state=False, therm_initial_state=False,
s=[1] * 2, s=[1] * 2,
#ε_init=.1, ε_init=.1,
δ_init=1
) )
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])
@ -169,7 +170,7 @@ ts = np.linspace(0,50,1000)
fig, ax = fs.plot_complex(ts, model.bcf(0)(ts)) fig, ax = fs.plot_complex(ts, model.bcf(0)(ts))
fs.plot_complex(ts, model.thermal_correlations(1)(ts)) fs.plot_complex(ts, model.thermal_correlations(1)(ts))
proc = model.thermal_process(0) proc = model.thermal_process(1)
import hops import hops
z=hops.core.utility.uni_to_gauss(np.random.rand(proc.get_num_y() * 2)) z=hops.core.utility.uni_to_gauss(np.random.rand(proc.get_num_y() * 2))
proc.new_process(z) proc.new_process(z)
@ -184,7 +185,7 @@ vs = np.linspace(0.1, 10, 100)
plt.plot(vs, chi(vs, ω_0)) plt.plot(vs, chi(vs, ω_0))
plt.plot(vs, G_h(vs)) plt.plot(vs, G_h(vs))
aux.integrate(model, 1) aux.integrate(model, 100)
_, ax = fs.plot_energy_overview(model, markersize=1, ensemble_args=dict(gc_sleep=0)) _, ax = fs.plot_energy_overview(model, markersize=1, ensemble_args=dict(gc_sleep=0))

49
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_2022-06-23_16-37-40_204409_252420/sockets/plasma_store', 'raylet_socket_name': '/tmp/ray/session_2022-06-23_16-37-40_204409_252420/sockets/raylet', 'webui_url': '', 'session_dir': '/tmp/ray/session_2022-06-23_16-37-40_204409_252420', 'metrics_export_port': 63012, 'gcs_address': '141.30.17.225:54143', 'address': '141.30.17.225:54143', 'node_id': '2f920e3b42f4aa5083c5586f173cc82069674783e95eafbbf03f71b5'}) : 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_2022-06-24_10-53-49_514411_273091/sockets/plasma_store', 'raylet_socket_name': '/tmp/ray/session_2022-06-24_10-53-49_514411_273091/sockets/raylet', 'webui_url': '', 'session_dir': '/tmp/ray/session_2022-06-24_10-53-49_514411_273091', 'metrics_export_port': 53492, 'gcs_address': '141.30.17.225:45555', 'address': '141.30.17.225:45555', 'node_id': 'dbb3ad75d477fb16831ea15ae7bdfb8839dd6d8a5c34b13f1651c4ea'})
#+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
@ -178,7 +178,8 @@ Init ray and silence stocproc.
switch_cycles=1, switch_cycles=1,
therm_initial_state=False, therm_initial_state=False,
s=[1] * 2, s=[1] * 2,
#ε_init=.1, ε_init=.1,
δ_init=1
) )
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])
@ -209,8 +210,8 @@ Let's test the assumptions of the paper.
#+RESULTS: #+RESULTS:
:RESULTS: :RESULTS:
| <matplotlib.lines.Line2D | at | 0x7f54a08ecca0> | | <matplotlib.lines.Line2D | at | 0x7fe0347c2250> |
[[file:./.ob-jupyter/89abe6a4bf84190aac8b4ad313b3d314f24b702c.svg]] [[file:./.ob-jupyter/5d81693c2e5ff1e9687e5e70e5180d641abd1dc3.svg]]
:END: :END:
#+begin_src jupyter-python :tangle nil #+begin_src jupyter-python :tangle nil
@ -228,8 +229,8 @@ Let's test the assumptions of the paper.
#+RESULTS: #+RESULTS:
:RESULTS: :RESULTS:
| <matplotlib.lines.Line2D | at | 0x7f54a1447910> | | <matplotlib.lines.Line2D | at | 0x7f54a0c921f0> |
[[file:./.ob-jupyter/2f4bb742fd68fa025f23f99e0e06df87a94feb45.svg]] [[file:./.ob-jupyter/3cd2693550d522629d7472e73e4dc97b6bd8c552.svg]]
:END: :END:
#+begin_src jupyter-python #+begin_src jupyter-python
@ -241,12 +242,12 @@ Let's test the assumptions of the paper.
#+RESULTS: #+RESULTS:
:RESULTS: :RESULTS:
| <Figure | size | 432x288 | with | 1 | Axes> | <AxesSubplot:> | | <Figure | size | 432x288 | with | 1 | Axes> | <AxesSubplot:> |
[[file:./.ob-jupyter/a08788b7160e9b1347a0835773dede97173fbfff.svg]] [[file:./.ob-jupyter/9edc7acf494129f3df0c94e05cd48c7ba0b6c1ba.svg]]
[[file:./.ob-jupyter/43aa665cc7b25e81c83a26a6e4af5b21c03223c4.svg]] [[file:./.ob-jupyter/685c7c43745ed82b661cd7278414d5b34ad6e6d8.svg]]
:END: :END:
#+begin_src jupyter-python #+begin_src jupyter-python
proc = model.thermal_process(0) proc = model.thermal_process(1)
import hops import hops
z=hops.core.utility.uni_to_gauss(np.random.rand(proc.get_num_y() * 2)) z=hops.core.utility.uni_to_gauss(np.random.rand(proc.get_num_y() * 2))
proc.new_process(z) proc.new_process(z)
@ -256,7 +257,7 @@ Let's test the assumptions of the paper.
#+RESULTS: #+RESULTS:
:RESULTS: :RESULTS:
| <Figure | size | 432x288 | with | 1 | Axes> | <AxesSubplot:> | | <Figure | size | 432x288 | with | 1 | Axes> | <AxesSubplot:> |
[[file:./.ob-jupyter/2088301f5f415fe36b0aa67f2ee589fa3ebf382a.svg]] [[file:./.ob-jupyter/e8dc2fc532b69162015e8390d3a9b976fdefde53.svg]]
:END: :END:
@ -275,22 +276,23 @@ Let's test the assumptions of the paper.
#+RESULTS: #+RESULTS:
:RESULTS: :RESULTS:
| <matplotlib.lines.Line2D | at | 0x7f54a10a4430> | | <matplotlib.lines.Line2D | at | 0x7f54a00e7280> |
[[file:./.ob-jupyter/5d35962c0527bd86684f67665c63888f16c252a2.svg]] [[file:./.ob-jupyter/ac8d27d0c620c5cf37a809aa1c38486283d71c1e.svg]]
:END: :END:
** TODO Integration ** TODO Integration
#+begin_src jupyter-python #+begin_src jupyter-python
aux.integrate(model, 1) aux.integrate(model, 100)
#+end_src #+end_src
#+RESULTS: #+RESULTS:
: [INFO hops.core.integration 2169805] Choosing the nonlinear integrator. : [INFO hops.core.integration 252420] Choosing the nonlinear integrator.
: [INFO hops.core.integration 2169805] Using 4 integrators. : [INFO hops.core.integration 252420] Using 4 integrators.
: [INFO hops.core.integration 2169805] Some 1 trajectories have to be integrated. : [INFO hops.core.integration 252420] Some 1 trajectories have to be integrated.
: [INFO hops.core.integration 2169805] Using 1001 hierarchy states. : [INFO hops.core.integration 252420] Using 1001 hierarchy states.
: 100% 1/1 [01:15<00:00, 75.76s/it] : 0% 0/1 [00:00<?, ?it/s][INFO hops.core.signal_delay 252420] caught sig 'SIGINT'
: [INFO hops.core.signal_delay 252420] caught sig 'SIGINT'
#+begin_src jupyter-python #+begin_src jupyter-python
@ -304,14 +306,15 @@ Let's test the assumptions of the paper.
#+RESULTS: #+RESULTS:
:RESULTS: :RESULTS:
: <matplotlib.legend.Legend at 0x7f54a10501c0> : <matplotlib.legend.Legend at 0x7fe0469f1160>
[[file:./.ob-jupyter/22365ffc3296f506a4158e15abbc62a18d7f4ec4.svg]] [[file:./.ob-jupyter/6766f4a9b087f073bba9ed06a1e8b3d14fb21bc4.svg]]
:END: :END:
- **too fast decoupling kills it** - **too fast decoupling kills it**
- no anti-zeno effects without detuning? - no anti-zeno effects without detuning?
- **spectral separation seems to be important** or maybe it's just the coupling strenght - **spectral separation seems to be important** or maybe it's just the coupling strenght
- too long modulation kills it - too long modulation kills it
- interestingly the interactin energy does not really depend on the coupling -> thermal part dominates
#+begin_src jupyter-python #+begin_src jupyter-python
with aux.get_data(model) as data: with aux.get_data(model) as data:
@ -338,7 +341,7 @@ Let's test the assumptions of the paper.
: pts[1:N+1, 1] = dep1slice : pts[1:N+1, 1] = dep1slice
: /nix/store/av32kn66py1m065lq75rmmlxrsqvgbaa-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/av32kn66py1m065lq75rmmlxrsqvgbaa-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/bf05798c67a06f36ab9884a1798f14df32a22584.svg]] [[file:./.ob-jupyter/800909fda5d70fe951f3782e2f975bc1b9923dd3.svg]]
:END: :END:
- no steady state ... but we have to average... - no steady state ... but we have to average...
@ -351,8 +354,8 @@ Let's test the assumptions of the paper.
#+RESULTS: #+RESULTS:
:RESULTS: :RESULTS:
| <matplotlib.lines.Line2D | at | 0x7f54a1944ca0> | | <matplotlib.lines.Line2D | at | 0x7fe034e71a30> |
[[file:./.ob-jupyter/64fca5ab15f5589fd2a0bed2ecf56cd41beeb000.svg]] [[file:./.ob-jupyter/4282545679cf56de104736b89c1c3cbb5994f7d9.svg]]
:END: :END:
** TODO Power and Efficiency ** TODO Power and Efficiency