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https://github.com/vale981/master-thesis
synced 2025-03-05 10:01:43 -05:00
try detune
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2 changed files with 15 additions and 12 deletions
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@ -20,7 +20,7 @@ import logging
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logging_setup(logging.INFO, show_stocproc=False)
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def anti_zeno_engine(
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Δ=5, γ=0.1 / 2, ω_c=1, ω_0=6, ε=1e-3, ε_couple=1 / 2, n=6, cycle_scale=1, switch_cycles=3
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Δ=5, γ=0.1 / 2, ω_c=1, ω_0=6, ε=1e-3, ε_couple=1 / 2, n=6, cycle_scale=1, switch_cycles=3, detune = 0
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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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@ -36,7 +36,7 @@ def anti_zeno_engine(
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cycles = int(np.around(τ_bath / τ_mod * cycle_scale))
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τ_c = cycles * τ_mod
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detune = 0
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H_mat = 1 / 2 * (qt.sigmaz().full()) * ω_0
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Δ_switch = τ_mod * switch_cycles
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t_max = n * (τ_c + τ_off)
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@ -82,7 +82,7 @@ def anti_zeno_engine(
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return model, (τ_mod, τ_c, τ_bath, cycles, model.ω_s)
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model, params = anti_zeno_engine(ε=1/2, ε_couple=1e-10, n=2)
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model, params = anti_zeno_engine(ε=1/2, ε_couple=1e-10, n=2, detune=.5, ω_0=6.5)
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params
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@ -37,7 +37,7 @@ Init ray and silence stocproc.
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* Model Definition
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#+begin_src jupyter-python :results none
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def anti_zeno_engine(
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Δ=5, γ=0.1 / 2, ω_c=1, ω_0=6, ε=1e-3, ε_couple=1 / 2, n=6, cycle_scale=1, switch_cycles=3
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Δ=5, γ=0.1 / 2, ω_c=1, ω_0=6, ε=1e-3, ε_couple=1 / 2, n=6, cycle_scale=1, switch_cycles=3, detune = 0
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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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@ -53,7 +53,7 @@ Init ray and silence stocproc.
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cycles = int(np.around(τ_bath / τ_mod * cycle_scale))
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τ_c = cycles * τ_mod
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detune = 0
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H_mat = 1 / 2 * (qt.sigmaz().full()) * ω_0
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Δ_switch = τ_mod * switch_cycles
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t_max = n * (τ_c + τ_off)
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@ -103,12 +103,12 @@ Init ray and silence stocproc.
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* Model Definition
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#+begin_src jupyter-python
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model, params = anti_zeno_engine(ε=1/2, ε_couple=1e-10, n=2)
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model, params = anti_zeno_engine(ε=1/2, ε_couple=1e-10, n=2, detune=.5, ω_0=6.5)
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params
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#+end_src
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#+RESULTS:
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| 1.2566370614359172 | 348.08846601774906 | 347.6950495572053 | 277 | (0 10) |
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| 1.2566370614359172 | 348.08846601774906 | 347.6950495572053 | 277 | (0.0 11.0) |
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Let's test the assumptions of the paper.
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@ -128,8 +128,8 @@ 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 | 0x7f9607180cd0> |
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[[file:./.ob-jupyter/103f059a370864a94b9d48d67a3dbf9e93399722.svg]]
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| <matplotlib.lines.Line2D | at | 0x7f96068a9340> |
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[[file:./.ob-jupyter/c416c29dc1b7efcfe7db368831188c25e55c74a6.svg]]
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:END:
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#+begin_src jupyter-python :tangle nil
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@ -140,8 +140,8 @@ 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 | 0x7f96069872e0> |
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[[file:./.ob-jupyter/160f075c49948f61a1e0d3b9dc1e386d30f3543d.svg]]
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| <matplotlib.lines.Line2D | at | 0x7f96069d2340> |
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[[file:./.ob-jupyter/ecf0b1db51e0d2c424e05f4cd2ec33d9ceff13d7.svg]]
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:END:
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@ -273,6 +273,7 @@ Let's test the assumptions of the paper.
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:END:
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- **too fast decoupling kills it**
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- no anti-zeno effects without detuning?
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- **spectral separation seems to be important** or maybe it's just the coupling strenght
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- too long modulation kills it
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@ -327,7 +328,7 @@ We need the time points where we sample the total energy.
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#+end_src
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#+RESULTS:
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[[file:./.ob-jupyter/66981899ba65d026196d29f54e45b3663b179d43.svg]]
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[[file:./.ob-jupyter/3236f7b1181a4feca36cc963453861545149ff47.svg]]
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#+begin_src jupyter-python
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with aux.get_data(model) as data:
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@ -408,3 +409,5 @@ model, params = anti_zeno_engine(ε=1e-1, ε_couple=1/4)
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#+begin_src jupyter-python
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model, params = anti_zeno_engine(ε=1e-1, ε_couple=1e-3, n=3)
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#+end_src
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#+RESULTS:
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