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https://github.com/vale981/master-thesis
synced 2025-03-05 10:01:43 -05:00
introduce the split integration and analysis
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4 changed files with 91 additions and 43 deletions
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from model_cc import *
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print(model.all_energies_online("results.fifo"))
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Here we try to reproduce the anti zeno engine from the paper.
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* Boilerplate
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#+begin_src jupyter-python :results none
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#+begin_src jupyter-python :results none :tangle model_cc.py
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import figsaver as fs
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from hiro_models.one_qubit_model import QubitModelMutliBath, StocProcTolerances
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import hiro_models.model_auxiliary as aux
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@ -19,28 +19,18 @@ Here we try to reproduce the anti zeno engine from the paper.
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import plot_utils as pu
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#+end_src
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Init ray and silence stocproc.
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#+begin_src jupyter-python
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import ray
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ray.shutdown()
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ray.init()
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#+end_src
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#+RESULTS:
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: 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_2022-08-26_09-16-24_372243_434989/sockets/plasma_store', 'raylet_socket_name': '/tmp/ray/session_2022-08-26_09-16-24_372243_434989/sockets/raylet', 'webui_url': '', 'session_dir': '/tmp/ray/session_2022-08-26_09-16-24_372243_434989', 'metrics_export_port': 64820, 'gcs_address': '141.30.17.225:62541', 'address': '141.30.17.225:62541', 'node_id': 'b13e6e8fe3cb7e0a8e40b763d39febfe0bac4a37ea2d205950b6dc19'})
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#+begin_src jupyter-python :results none
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#+begin_src jupyter-python :results none :tangle model_cc.py
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from hops.util.logging_setup import logging_setup
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import logging
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logging_setup(logging.INFO, show_stocproc=False)
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#+end_src
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* Model Definition
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#+begin_src jupyter-python :results none
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#+begin_src jupyter-python :results none :tangle model_cc.py
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from anti_zeno_engine import *
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#+end_src
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#+begin_src jupyter-python
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#+begin_src jupyter-python :tangle model_cc.py
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(
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model,
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params
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@ -49,7 +39,7 @@ Init ray and silence stocproc.
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ε=1,#.1,
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ω_c=1,
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ε_couple=.68,
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n=35,
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n=60,
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detune=.5,
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ω_0=20,
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T_c=5e3,
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@ -58,7 +48,7 @@ Init ray and silence stocproc.
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γ=.2,
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switch_cycles=1,
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therm_initial_state=False,
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ε_init=.001/2,
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#ε_init=.001/2,
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terms=7,
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dt=0.01/2,
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sp_tol=1e-4,
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@ -70,40 +60,45 @@ Init ray and silence stocproc.
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#+RESULTS:
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#+begin_src jupyter-python :tangle integrate_cc.py
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from model_cc import *
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import ray
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ray.shutdown()
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ray.init()
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aux.integrate(model, 10, stream_file="results.fifo")
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#+end_src
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#+begin_src jupyter-python :tangle analyze_cc.py
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from model_cc import *
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print(model.all_energies_online("results.fifo"))
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#+end_src
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#+RESULTS:
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#+begin_src jupyter-python
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ts = model.t # np.linspace(0,10,1000)
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proc = model.thermal_process(1)
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import hops
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z=hops.core.utility.uni_to_gauss(np.random.rand(proc.get_num_y() * 2))
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proc.new_process(z)
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pu.plot_complex(ts, proc(ts) * model.bcf_scales[1])
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flow, interaction, int_pow, system, sys_pow = model.all_energies_online_from_cache()
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fig, ax = plt.subplots()
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print(sys_pow.N)
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pu.plot_with_σ(model.t, (sys_pow+int_pow.sum_baths()).integrate(model.t), ax=ax)
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pu.plot_with_σ(model.t, (-1 * flow).sum_baths().integrate(model.t) + interaction.sum_baths() + system + 10, ax=ax)
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# with aux.get_data(model) as data:
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# #pu.plot_with_σ(model.t, model.total_energy_from_power(data), ax=ax)
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# pu.plot_with_σ(model.t, model.interaction_energy(data).sum_baths() - interaction.sum_baths(), ax=ax)
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# pu.plot_with_σ(model.t, model.interaction_power(data).sum_baths() - int_pow.sum_baths(), ax=ax)
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# pu.plot_with_σ(model.t, model.system_energy(data).sum_baths() - system.sum_baths(), ax=ax)
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# pu.plot_with_σ(model.t, model.system_power(data).sum_baths() - sys_pow.sum_baths(), ax=ax)
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# pu.plot_with_σ(model.t, model.bath_energy_flow(data).sum_baths() - flow.sum_baths(), ax=ax)
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#+end_src
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#+RESULTS:
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:RESULTS:
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| <Figure | size | 520x320 | with | 1 | Axes> | <AxesSubplot:> |
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[[file:./.ob-jupyter/458ed32f5b39dff70d717cc2593889573946a962.svg]]
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: 10
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| hline | <AxesSubplot:> | ((<matplotlib.lines.Line2D at 0x7fec3f75eb80>) <matplotlib.collections.PolyCollection at 0x7fec3f75e9d0>) |
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[[file:./.ob-jupyter/72099bcb22465077395cd8acccbecc8f6e768f84.svg]]
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:END:
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#+begin_src jupyter-python
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params.cycles
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#+end_src
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#+RESULTS:
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: 10
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#+begin_src jupyter-python
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aux.integrate(model, 10)
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#+end_src
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#+RESULTS:
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: [INFO hops.core.integration 14258] Choosing the nonlinear integrator.
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: [INFO hops.core.integration 14258] Using 8 integrators.
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: [INFO hops.core.integration 14258] Some 9 trajectories have to be integrated.
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: [INFO hops.core.integration 14258] Using 680 hierarchy states.
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: 0% 0/9 [00:00<?, ?it/s]
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#+begin_src jupyter-python
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plot_az_coupling_diagram(model, params)
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#+end_src
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@ -111,7 +106,7 @@ Init ray and silence stocproc.
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#+RESULTS:
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:RESULTS:
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| <Figure | size | 520x320 | with | 1 | Axes> | <AxesSubplot:xlabel= | $\tau$ | > |
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[[file:./.ob-jupyter/1f7ffd13448c6001e2f81fb22d1fa2c919b497ab.svg]]
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[[file:./.ob-jupyter/d1345d964ba6362247d8f34e3ea98e5b5f91a7db.svg]]
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:END:
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#+begin_src jupyter-python
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from model_cc import *
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import ray
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ray.shutdown()
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ray.init()
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aux.integrate(model, 10, stream_file="results.fifo")
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46
python/energy_flow_proper/10_antizeno_engine/model_cc.py
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python/energy_flow_proper/10_antizeno_engine/model_cc.py
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import figsaver as fs
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from hiro_models.one_qubit_model import QubitModelMutliBath, StocProcTolerances
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import hiro_models.model_auxiliary as aux
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from hops.util.utilities import relative_entropy, relative_entropy_single, entropy, trace_distance
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from hopsflow.util import EnsembleValue
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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, Shift
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import matplotlib.pyplot as plt
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import numpy as np
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import matplotlib
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import plot_utils as pu
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from hops.util.logging_setup import logging_setup
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import logging
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logging_setup(logging.INFO, show_stocproc=False)
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from anti_zeno_engine import *
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(
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model,
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params
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) = anti_zeno_engine(
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Δ=11,
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ε=1,#.1,
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ω_c=1,
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ε_couple=.68,
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n=60,
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detune=.5,
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ω_0=20,
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T_c=5e3,
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T_h=5e4,
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δ=[3.2*.01/10, 1.08*.01/10],
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γ=.2,
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switch_cycles=1,
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therm_initial_state=False,
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#ε_init=.001/2,
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terms=7,
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dt=0.01/2,
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sp_tol=1e-4,
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init_time_steps=10,
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interpolation_multiplier=10,
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)
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model.k_max = 3
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