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https://github.com/vale981/master-thesis
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orientate on the paper
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2 changed files with 29 additions and 21 deletions
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@ -32,7 +32,8 @@ def anti_zeno_engine(
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detune=0,
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δ=[2] * 2,
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T_h=5,
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therm_initial_state=False
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therm_initial_state=False,
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s=1
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):
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# τ_bath = 1 / ω_c
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τ_mod = 2 * np.pi / Δ
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@ -97,15 +98,15 @@ def anti_zeno_engine(
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therm_methods=["tanhsinh", "fft"],
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)
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return model, Δ, (τ_mod, τ_c, τ_bath, cycles, model.ω_s)
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return model, Δ, (τ_mod, τ_c, τ_bath, cycles, model.ω_s, ω_0, τ_mod)
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model, Δ, params = anti_zeno_engine(ε=1/10, ε_couple=1/2, n=10, detune=4, ω_0 = 10, δ=[2]*2, γ=.05/2, therm_initial_state=True)
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model, Δ, (τ_mod, τ_c, τ_bath, cycles, model.ω_s, ω_0, τ_s) = anti_zeno_engine(Δ = 15, ε=1/6, ω_c=1, ε_couple=.7, n=5, detune=-1, ω_0 = 20, δ=[2]*2, γ=.2, therm_initial_state=True)
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#model, params = anti_zeno_engine(ε=1/2, ε_couple=1e-4, n=1, detune=.5, δ=[.1,.1])
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params
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cycles
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aux.integrate(model, 5000)
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aux.integrate(model, 1000)
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_, ax = fs.plot_energy_overview(model, markersize=1)
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# with aux.get_data(model) as data:
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@ -48,7 +48,8 @@ Init ray and silence stocproc.
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detune=0,
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δ=[2] * 2,
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T_h=5,
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therm_initial_state=False
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therm_initial_state=False,
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s=1
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):
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# τ_bath = 1 / ω_c
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τ_mod = 2 * np.pi / Δ
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@ -113,13 +114,13 @@ Init ray and silence stocproc.
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therm_methods=["tanhsinh", "fft"],
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)
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return model, Δ, (τ_mod, τ_c, τ_bath, cycles, model.ω_s)
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return model, Δ, (τ_mod, τ_c, τ_bath, cycles, model.ω_s, ω_0, τ_mod)
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#+end_src
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* Model Definition
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#+begin_src jupyter-python
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model, Δ, params = anti_zeno_engine(ε=1/10, ε_couple=1/2, n=10, detune=4, ω_0 = 10, δ=[2]*2, γ=.05/2, therm_initial_state=True)
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model, Δ, (τ_mod, τ_c, τ_bath, cycles, model.ω_s, ω_0, τ_s) = anti_zeno_engine(Δ = 15, ε=1/6, ω_c=1, ε_couple=.7, n=5, detune=-1, ω_0 = 20, δ=[2]*2, γ=.2, therm_initial_state=True)
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#model, params = anti_zeno_engine(ε=1/2, ε_couple=1e-4, n=1, detune=.5, δ=[.1,.1])
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params
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#+end_src
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@ -129,10 +130,13 @@ Init ray and silence stocproc.
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Let's test the assumptions of the paper.
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#+begin_src jupyter-python :results none
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#+begin_src jupyter-python
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cycles
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#+end_src
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#+RESULTS:
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: 13
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** BCFs and Modulations
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#+begin_src jupyter-python :tangle nil
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def thermal_bcf(t):
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@ -145,28 +149,31 @@ Let's test the assumptions of the paper.
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#+RESULTS:
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:RESULTS:
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| <matplotlib.lines.Line2D | at | 0x7f5d260e80a0> |
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[[file:./.ob-jupyter/726ed9600beddd1075ec34af565217476ba75ded.svg]]
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| <matplotlib.lines.Line2D | at | 0x7f5cd835aa30> |
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[[file:./.ob-jupyter/58c1849bdc141b930cd11e4a0772548a750999af.svg]]
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:END:
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#+begin_src jupyter-python :tangle nil
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ωs = np.linspace(0, 20, 1000)
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plt.plot(ωs, model.spectral_density(0)(ωs))
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plt.plot(ωs, model.spectral_density(1)(ωs))
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ωs = np.linspace(0, 40, 10000)
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mx = model.spectral_density(0)(ωs).max()
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plt.plot(ωs, model.spectral_density(0)(ωs)/mx)
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plt.plot(ωs, model.spectral_density(1)(ωs)/mx)
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plt.axvline(model.H.operator_norm(0) * 2 + Δ)
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plt.axvline(model.H.operator_norm(0) * 2 - Δ)
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plt.plot(ωs, np.sinc((ωs - ω_0 + Δ) * τ_s * cycles))
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plt.plot(ωs, np.sinc((ωs - ω_0 - Δ) * τ_s * cycles))
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#+end_src
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#+RESULTS:
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:RESULTS:
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: <matplotlib.lines.Line2D at 0x7f5d260d9940>
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[[file:./.ob-jupyter/fd95c61584398b96afe53c29cb8817e37a58f8bf.svg]]
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| <matplotlib.lines.Line2D | at | 0x7f5cd83dcca0> |
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[[file:./.ob-jupyter/113b340dc8e93c0b6d1687dc2a92b9f3e746509b.svg]]
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:END:
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** TODO Integration
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#+begin_src jupyter-python
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aux.integrate(model, 5000)
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aux.integrate(model, 1000)
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#+end_src
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@ -184,7 +191,7 @@ Let's test the assumptions of the paper.
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#+begin_example
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[0;31m---------------------------------------------------------------------------[0m
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[0;31mRuntimeError[0m Traceback (most recent call last)
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Input [0;32mIn [69][0m, in [0;36m<cell line: 1>[0;34m()[0m
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Input [0;32mIn [176][0m, in [0;36m<cell line: 1>[0;34m()[0m
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[0;32m----> 1[0m _, ax [38;5;241m=[39m [43mfs[49m[38;5;241;43m.[39;49m[43mplot_energy_overview[49m[43m([49m[43mmodel[49m[43m,[49m[43m [49m[43mmarkersize[49m[38;5;241;43m=[39;49m[38;5;241;43m1[39;49m[43m)[49m
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[1;32m 2[0m [38;5;66;03m# with aux.get_data(model) as data:[39;00m
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[1;32m 3[0m [38;5;66;03m# fs.plot_with_σ(model.t, model.total_energy(data), ax=ax)[39;00m
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@ -207,9 +214,9 @@ Let's test the assumptions of the paper.
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[1;32m 145[0m [38;5;28;01melse[39;00m:
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[0;32m--> 146[0m [38;5;28;01mraise[39;00m [38;5;167;01mRuntimeError[39;00m([38;5;124mf[39m[38;5;124m"[39m[38;5;124mNo data found for model with hash [39m[38;5;124m'[39m[38;5;132;01m{[39;00mhexhash[38;5;132;01m}[39;00m[38;5;124m'[39m[38;5;124m.[39m[38;5;124m"[39m)
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[0;31mRuntimeError[0m: No data found for model with hash 'bd9e2510c88d49b8cb8938ff8510c4dd61a92ce8746d623e126886d9972ba9a0'.
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[0;31mRuntimeError[0m: No data found for model with hash 'da33647fd18c4384c29267fdef75d3a92854ae89e407f1c0e6d97499bf5462f9'.
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#+end_example
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[[file:./.ob-jupyter/5977a58c06c307c5917aa9e110ab85b47f060ba4.svg]]
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[[file:./.ob-jupyter/107b34b88483dd236391c2dc9151730a6d4fd8d5.svg]]
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:END:
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- **too fast decoupling kills it**
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