try longer coupling + larger detune

python/energy_flow_proper/10_antizeno_engine/10_first_anti_zeno.py

@@ -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,
     ε=1,
-    #ε=.7,
     ω_c=1,
-    ε_couple=0.6,
+    ε_couple=0.2,
     n=6,
-    detune=-0.8,
+    detune=-0.9,
     ω_0=20,
     T_c=8,
     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,
     switch_cycles=1,
     therm_initial_state=False,
-    s=[1]*2,
-    ε_init=.2,
-    terms = 6,
-    dt=.01
+    s=[1] * 2,
+    ε_init=0.2,
+    terms=6,
+    dt=0.01,
 )
 model.k_max = 4
 # 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:
   _, 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=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), 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(data), ax=ax)
   #fs.plot_with_σ(model.t, model.interaction_energy(data).for_bath(1), ax=ax)

python/energy_flow_proper/10_antizeno_engine/anti_zeno_engine.org

@@ -25,7 +25,7 @@ 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_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
   from hops.util.logging_setup import logging_setup
@@ -193,14 +193,17 @@ Init ray and silence stocproc.
 
 * Model Definition
 #+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,
       ε=1,
-      #ε=.7,
       ω_c=1,
-      ε_couple=0.6,
+      ε_couple=0.2,
       n=6,
-      detune=-0.8,
+      detune=-0.9,
       ω_0=20,
       T_c=8,
       T_h=40,
@@ -208,10 +211,10 @@ Init ray and silence stocproc.
       γ=5 / 10,
       switch_cycles=1,
       therm_initial_state=False,
-      s=[1]*2,
-      ε_init=.2,
-      terms = 6,
-      dt=.01
+      s=[1] * 2,
+      ε_init=0.2,
+      terms=6,
+      dt=0.01,
   )
   model.k_max = 4
   # 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
 
 #+RESULTS:
-: 6
+: 19
 
 ** BCFs and Modulations
 #+begin_src jupyter-python :tangle nil
@@ -242,8 +245,8 @@ Let's test the assumptions of the paper.
 
 #+RESULTS:
 :RESULTS:
-| <matplotlib.lines.Line2D | at | 0x7f488b48df40> |
-[[file:./.ob-jupyter/8723c6af359ceab18de1610520d92b9fdd02be5b.svg]]
+| <matplotlib.lines.Line2D | at | 0x7fc87a521700> |
+[[file:./.ob-jupyter/5ea0a35cd05cf2cf4c0126978e35a10d11416e28.svg]]
 :END:
 
 #+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 * 2), color="orange", linewidth=.5)
   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
 
 #+RESULTS:
 :RESULTS:
-| <matplotlib.lines.Line2D | at | 0x7fefdd8a7670> |
-[[file:./.ob-jupyter/89fa9d020cfafcb0ae5f7de1a41babd430954a82.svg]]
+| 20.0 | 30.0 |
+[[file:./.ob-jupyter/3a1786b8d1a6a7908c454f193523beda1ce82a2e.svg]]
 :END:
 
 #+begin_src jupyter-python
@@ -343,8 +346,8 @@ Let's test the assumptions of the paper.
 
 #+RESULTS:
 :RESULTS:
-: <matplotlib.legend.Legend at 0x7f4899b689d0>
-[[file:./.ob-jupyter/9b1a1b35f9ce833fe41b19d8d50c18f6c85047fd.svg]]
+: <matplotlib.legend.Legend at 0x7fc87c4a29a0>
+[[file:./.ob-jupyter/937986fe8e46f733a643430ae62eb572ff64fca6.svg]]
 :END:
 
 - **too fast decoupling kills it**
@@ -382,7 +385,7 @@ Let's test the assumptions of the paper.
 :   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
 :   pts[N+2:, 1] = dep2slice[::-1]
-[[file:./.ob-jupyter/d106ce5fe0fb377b978239e7e68b86a87ba225ab.svg]]
+[[file:./.ob-jupyter/cd0c69bb0ecbf66e8154d4fe2875f53655d72e29.svg]]
 :END:
 
 - no steady state ... but we have to average...
@@ -395,8 +398,8 @@ Let's test the assumptions of the paper.
 
 #+RESULTS:
 :RESULTS:
-| <matplotlib.lines.Line2D | at | 0x7f488b950a30> |
-[[file:./.ob-jupyter/b055915031dec0963da129357ed8ce92b51d591d.svg]]
+| <matplotlib.lines.Line2D | at | 0x7fc8905f7910> |
+[[file:./.ob-jupyter/1f66c12d5d63678125a0ba2775363698d48f3f74.svg]]
 :END:
 
 ** TODO Power and Efficiency
@@ -407,9 +410,9 @@ We need the time points where we sample the total energy.
 #+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=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.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(data), 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
 
 #+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:
+: -0.03996888620839219
+[[file:./.ob-jupyter/c1504df12b138f8ca873b863d4bf605aa127341b.svg]]
+:END:
+
+
+
 : -0.03951090584819158
 [[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:
-: 6.41113197470654e-05
-[[file:./.ob-jupyter/1cf181c3eceb944bc365a27a5cd5f6363e1ce7b6.svg]]
+: 5.741482766795489e-05
+[[file:./.ob-jupyter/97d96e8931b5b208c905061d9e9ee7a15b401324.svg]]
 :END: