mirror of
https://github.com/vale981/master-thesis
synced 2025-03-04 17:41:43 -05:00
add init time
This commit is contained in:
parent
f2f58cc0a0
commit
a144caad39
2 changed files with 107 additions and 41 deletions
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@ -7,7 +7,7 @@ import numpy as np
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import qutip as qt
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import scipy
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import utilities as ut
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from hops.util.dynamic_matrix import SmoothStep, Periodic, Harmonic, ConstantMatrix, Piecewise
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from hops.util.dynamic_matrix import SmoothStep, Periodic, Harmonic, ConstantMatrix, Piecewise, Shift
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import matplotlib.pyplot as plt
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import numpy as np
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@ -34,8 +34,10 @@ def anti_zeno_engine(
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T_h=5,
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therm_initial_state=False,
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s=1,
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dt=.1,
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T_c=0,
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dt=0.1,
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T_c=0,
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ε_init=0,
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δ_init=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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@ -77,6 +79,37 @@ def anti_zeno_engine(
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τ_c + τ_off,
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)
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if ε_init > 0:
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initializing_period = -np.log(ε_init) / smallest_exponent
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t_max += initializing_period
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H = Piecewise(
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[
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ConstantMatrix(H_mat),
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Shift(
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H,
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initializing_period,
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),
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],
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[0, initializing_period, np.inf],
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)
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L = Piecewise(
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[
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(ConstantMatrix(L_mat)
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- SmoothStep(
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L_mat,
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initializing_period - τ_off - Δ_switch,
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initializing_period - τ_off,
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2,
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)) * np.sqrt(δ_init),
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Shift(
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L,
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initializing_period,
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),
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],
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[0, initializing_period, np.inf],
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)
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model = QubitModelMutliBath(
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δ=δ,
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ω_c=[ω_c, ω_c],
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@ -101,7 +134,11 @@ def anti_zeno_engine(
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s=s,
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)
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return model, Δ, (τ_mod, τ_c, τ_bath, cycles, model.ω_s, ω_0, τ_mod, τ_off, n, Δ_switch)
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return (
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model,
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Δ,
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(τ_mod, τ_c, τ_bath, cycles, model.ω_s, ω_0, τ_mod, τ_off, n, Δ_switch),
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)
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model, Δ, (τ_mod, τ_c, τ_bath, cycles, model.ω_s, ω_0, τ_s, τ_off, n, Δ_switch) = anti_zeno_engine(
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Δ=1,
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@ -118,6 +155,8 @@ model, Δ, (τ_mod, τ_c, τ_bath, cycles, model.ω_s, ω_0, τ_s, τ_off, n, Δ
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switch_cycles=1,
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therm_initial_state=False,
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s=[1] * 2,
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ε_init=.1,
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δ_init=5,
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)
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model.k_max = 4
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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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@ -171,13 +210,13 @@ with aux.get_data(model) as data:
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_, ax = plt.subplots()
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# fs.plot_with_σ(model.t, model.bath_energy(data), bath=0, ax=ax)
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# fs.plot_with_σ(model.t, model.bath_energy(data), bath=1, ax=ax)
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# fs.plot_with_σ(model.t, model.bath_energy(data).sum_baths(), ax=ax)
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#fs.plot_with_σ(model.t, model.bath_energy(data).sum_baths(), ax=ax)
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# fs.plot_with_σ(model.t, model.total_energy(data), ax=ax)
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fs.plot_with_σ(model.t, model.interaction_energy(data).for_bath(1), ax=ax)
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#fs.plot_with_σ(model.t, model.interaction_energy(data).for_bath(1), ax=ax)
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#fs.plot_with_σ(model.t, model.system_energy(data), ax=ax)
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#fs.plot_with_σ(model.t, model.system_energy(data) + model.bath_energy(data).sum_baths(), ax=ax)
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fs.plot_with_σ(model.t, model.system_energy(data) + model.bath_energy(data).sum_baths(), ax=ax)
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# ax.plot(model.t, ut.smoothen(model.t, model.total_energy(data).value, frac=.1))
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# ax.plot(model.t, np.gradient(model.total_energy(data).value))
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@ -12,7 +12,7 @@ Here we try to reproduce the anti zeno engine from the paper.
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import qutip as qt
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import scipy
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import utilities as ut
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from hops.util.dynamic_matrix import SmoothStep, Periodic, Harmonic, ConstantMatrix, Piecewise
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from hops.util.dynamic_matrix import SmoothStep, Periodic, Harmonic, ConstantMatrix, Piecewise, Shift
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import matplotlib.pyplot as plt
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import numpy as np
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#+end_src
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@ -25,7 +25,7 @@ Init ray and silence stocproc.
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#+end_src
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#+RESULTS:
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: RayContext(dashboard_url='', python_version='3.9.12', ray_version='1.12.1', ray_commit='4863e33856b54ccf8add5cbe75e41558850a1b75', 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_2022-06-20_11-11-50_409350_1559734/sockets/plasma_store', 'raylet_socket_name': '/tmp/ray/session_2022-06-20_11-11-50_409350_1559734/sockets/raylet', 'webui_url': '', 'session_dir': '/tmp/ray/session_2022-06-20_11-11-50_409350_1559734', 'metrics_export_port': 48028, 'gcs_address': '141.30.17.225:57122', 'address': '141.30.17.225:57122', 'node_id': '4e032ba66cd47b05f939f5a1a89a9b5ecd50a752e9f61b648868067b'})
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: RayContext(dashboard_url='', python_version='3.9.12', ray_version='1.12.1', ray_commit='4863e33856b54ccf8add5cbe75e41558850a1b75', 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_2022-06-20_16-04-05_747504_1622147/sockets/plasma_store', 'raylet_socket_name': '/tmp/ray/session_2022-06-20_16-04-05_747504_1622147/sockets/raylet', 'webui_url': '', 'session_dir': '/tmp/ray/session_2022-06-20_16-04-05_747504_1622147', 'metrics_export_port': 64315, 'gcs_address': '141.30.17.225:45941', 'address': '141.30.17.225:45941', 'node_id': 'f8e5ee19d6c4bb61c86fcbf2ad3d4eb02d0f22821152e4a65bab8302'})
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#+begin_src jupyter-python :results none
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from hops.util.logging_setup import logging_setup
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@ -50,8 +50,10 @@ Init ray and silence stocproc.
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T_h=5,
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therm_initial_state=False,
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s=1,
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dt=.1,
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T_c=0,
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dt=0.1,
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T_c=0,
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ε_init=0,
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δ_init=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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@ -93,6 +95,37 @@ Init ray and silence stocproc.
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τ_c + τ_off,
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)
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if ε_init > 0:
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initializing_period = -np.log(ε_init) / smallest_exponent
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t_max += initializing_period
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H = Piecewise(
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[
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ConstantMatrix(H_mat),
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Shift(
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H,
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initializing_period,
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),
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],
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[0, initializing_period, np.inf],
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)
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L = Piecewise(
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[
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(ConstantMatrix(L_mat)
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- SmoothStep(
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L_mat,
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initializing_period - τ_off - Δ_switch,
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initializing_period - τ_off,
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2,
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)) * np.sqrt(δ_init),
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Shift(
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L,
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initializing_period,
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),
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],
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[0, initializing_period, np.inf],
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)
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model = QubitModelMutliBath(
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δ=δ,
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ω_c=[ω_c, ω_c],
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@ -117,7 +150,11 @@ Init ray and silence stocproc.
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s=s,
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)
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return model, Δ, (τ_mod, τ_c, τ_bath, cycles, model.ω_s, ω_0, τ_mod, τ_off, n, Δ_switch)
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return (
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model,
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Δ,
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(τ_mod, τ_c, τ_bath, cycles, model.ω_s, ω_0, τ_mod, τ_off, n, Δ_switch),
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)
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#+end_src
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@ -138,6 +175,8 @@ Init ray and silence stocproc.
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switch_cycles=1,
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therm_initial_state=False,
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s=[1] * 2,
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ε_init=.1,
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δ_init=5,
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)
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model.k_max = 4
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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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@ -168,8 +207,8 @@ 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 | 0x7f807afacd00> |
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[[file:./.ob-jupyter/b5058d982594e3c04b37fe39843ac77068575bd6.svg]]
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| <matplotlib.lines.Line2D | at | 0x7fd6c477d940> |
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[[file:./.ob-jupyter/756fc4868ff15b727fe451ef7edd626f6fac267a.svg]]
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:END:
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#+begin_src jupyter-python :tangle nil
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@ -188,7 +227,7 @@ Let's test the assumptions of the paper.
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#+RESULTS:
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:RESULTS:
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| 2.75 | 3.2 |
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[[file:./.ob-jupyter/9b687202bdbd2222a968384feea3b7a1cfac5af4.svg]]
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[[file:./.ob-jupyter/867ddd4e8f5b15b75dc0293d1e626fd26279ec9a.svg]]
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:END:
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#+begin_src jupyter-python
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@ -200,8 +239,8 @@ Let's test the assumptions of the paper.
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#+RESULTS:
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:RESULTS:
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| <Figure | size | 432x288 | with | 1 | Axes> | <AxesSubplot:> |
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[[file:./.ob-jupyter/4c53103cb8c40d42c350d174bcba82f9eb89326d.svg]]
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[[file:./.ob-jupyter/a97d825bfa17726c6cb33b4169eb9e47ad68f5fc.svg]]
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[[file:./.ob-jupyter/f377ba31bfd3b2c137b8867c6d7e3920e1d9da09.svg]]
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[[file:./.ob-jupyter/06b24d015c4513312e089b3bf76dd8406bd4f491.svg]]
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:END:
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#+begin_src jupyter-python
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@ -215,7 +254,7 @@ Let's test the assumptions of the paper.
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#+RESULTS:
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:RESULTS:
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| <Figure | size | 432x288 | with | 1 | Axes> | <AxesSubplot:> |
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[[file:./.ob-jupyter/07ad687ed4242e118fe10e9994e767ff655a0a5d.svg]]
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[[file:./.ob-jupyter/42a6b32dc296978e6e491d29c340d522dbf2fa1e.svg]]
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:END:
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@ -256,8 +295,8 @@ Let's test the assumptions of the paper.
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#+RESULTS:
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:RESULTS:
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: <matplotlib.legend.Legend at 0x7f807b65ee50>
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[[file:./.ob-jupyter/8a49649ab76a2e7cceab3136ef1a47aec4dbd046.svg]]
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: <matplotlib.legend.Legend at 0x7fd6c5f0b550>
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[[file:./.ob-jupyter/03d799944bfd6428a9dde1bd29dc69eaaf066383.svg]]
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:END:
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- **too fast decoupling kills it**
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@ -279,19 +318,7 @@ Let's test the assumptions of the paper.
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#+end_src
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#+RESULTS:
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:RESULTS:
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: /nix/store/pwhaggpgvxhy410r6vlx448v9lp8n08s-python3-3.9.12-env/lib/python3.9/site-packages/matplotlib/cbook/__init__.py:1298: ComplexWarning: Casting complex values to real discards the imaginary part
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: return np.asarray(x, float)
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: /nix/store/pwhaggpgvxhy410r6vlx448v9lp8n08s-python3-3.9.12-env/lib/python3.9/site-packages/matplotlib/axes/_axes.py:5218: ComplexWarning: Casting complex values to real discards the imaginary part
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: pts[0] = start
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: /nix/store/pwhaggpgvxhy410r6vlx448v9lp8n08s-python3-3.9.12-env/lib/python3.9/site-packages/matplotlib/axes/_axes.py:5219: ComplexWarning: Casting complex values to real discards the imaginary part
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: pts[N + 1] = end
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: /nix/store/pwhaggpgvxhy410r6vlx448v9lp8n08s-python3-3.9.12-env/lib/python3.9/site-packages/matplotlib/axes/_axes.py:5222: ComplexWarning: Casting complex values to real discards the imaginary part
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: pts[1:N+1, 1] = dep1slice
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: /nix/store/pwhaggpgvxhy410r6vlx448v9lp8n08s-python3-3.9.12-env/lib/python3.9/site-packages/matplotlib/axes/_axes.py:5224: ComplexWarning: Casting complex values to real discards the imaginary part
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: pts[N+2:, 1] = dep2slice[::-1]
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[[file:./.ob-jupyter/a365ba2355e7f4d1d260b557aad9ec771b7e45a0.svg]]
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:END:
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[[file:./.ob-jupyter/8eae8f664adef59ed6a98a7a67e37d440b01d028.svg]]
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- no steady state ... but we have to average...
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@ -304,8 +331,8 @@ 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 | 0x7f807bc37ac0> |
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[[file:./.ob-jupyter/6fa097dddc1db3a222e646106b94269eac3bc844.svg]]
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| <matplotlib.lines.Line2D | at | 0x7fd6c4b76340> |
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[[file:./.ob-jupyter/6cb93ce5aefd3b291f4901919b5459ffee4d0b63.svg]]
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:END:
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** TODO Power and Efficiency
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@ -318,19 +345,19 @@ We need the time points where we sample the total energy.
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_, ax = plt.subplots()
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# fs.plot_with_σ(model.t, model.bath_energy(data), bath=0, ax=ax)
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# fs.plot_with_σ(model.t, model.bath_energy(data), bath=1, ax=ax)
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# fs.plot_with_σ(model.t, model.bath_energy(data).sum_baths(), ax=ax)
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#fs.plot_with_σ(model.t, model.bath_energy(data).sum_baths(), ax=ax)
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# fs.plot_with_σ(model.t, model.total_energy(data), ax=ax)
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fs.plot_with_σ(model.t, model.interaction_energy(data).for_bath(1), ax=ax)
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#fs.plot_with_σ(model.t, model.interaction_energy(data).for_bath(1), ax=ax)
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#fs.plot_with_σ(model.t, model.system_energy(data), ax=ax)
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#fs.plot_with_σ(model.t, model.system_energy(data) + model.bath_energy(data).sum_baths(), ax=ax)
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fs.plot_with_σ(model.t, model.system_energy(data) + model.bath_energy(data).sum_baths(), ax=ax)
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# ax.plot(model.t, ut.smoothen(model.t, model.total_energy(data).value, frac=.1))
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# ax.plot(model.t, np.gradient(model.total_energy(data).value))
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#+end_src
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#+RESULTS:
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[[file:./.ob-jupyter/0a54c6e1264f39ecb1e5e6febfa88ff11eb31f91.svg]]
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[[file:./.ob-jupyter/050e6865a23ef19d197a61c8c544e55c8e85c600.svg]]
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@ -364,8 +391,8 @@ One cycle power.
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#+RESULTS:
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:RESULTS:
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: -0.00047898251459689074
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[[file:./.ob-jupyter/db74d50ce5db826ead3350c2087e8b3ec016985b.svg]]
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: 8.593886729920876e-05
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[[file:./.ob-jupyter/ad56687b7247b3ba97ee566373fda4b0f7d7804f.svg]]
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:END:
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