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https://github.com/vale981/master-thesis
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otto: new settings
This commit is contained in:
parent
96e07cea12
commit
61be4d4fba
2 changed files with 93 additions and 40 deletions
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@ -23,7 +23,7 @@ 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='3.9.13', ray_version='1.13.0', ray_commit='e4ce38d001dbbe09cd21c497fedd03d692b2be3e', address_info={'node_ip_address': '192.168.100.170', 'raylet_ip_address': '192.168.100.170', 'redis_address': None, 'object_store_address': '/tmp/ray/session_2022-08-15_11-00-55_094481_7214/sockets/plasma_store', 'raylet_socket_name': '/tmp/ray/session_2022-08-15_11-00-55_094481_7214/sockets/raylet', 'webui_url': '', 'session_dir': '/tmp/ray/session_2022-08-15_11-00-55_094481_7214', 'metrics_export_port': 57106, 'gcs_address': '192.168.100.170:59797', 'address': '192.168.100.170:59797', 'node_id': '9026d4f7c22bee0643e9faa61b56e25e2a8eed0568554be81b1fafe2'})
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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': '192.168.100.170', 'raylet_ip_address': '192.168.100.170', 'redis_address': None, 'object_store_address': '/tmp/ray/session_2022-08-15_13-45-28_971048_59572/sockets/plasma_store', 'raylet_socket_name': '/tmp/ray/session_2022-08-15_13-45-28_971048_59572/sockets/raylet', 'webui_url': '', 'session_dir': '/tmp/ray/session_2022-08-15_13-45-28_971048_59572', 'metrics_export_port': 53281, 'gcs_address': '192.168.100.170:61047', 'address': '192.168.100.170:61047', 'node_id': '19344eac29e15d6404c14920662bd8259981954f0a9fde73ef5f8b4b'})
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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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@ -55,7 +55,7 @@ Let's lay down some basic functionality.
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#+begin_src jupyter-python :results none
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t_compression = .1
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t_expansion = .1
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comp_ratio = .5
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comp_ratio = .25
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hot_switch_ratio = .8
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cold_switch_ratio = .8
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@ -68,7 +68,6 @@ Let's lay down some basic functionality.
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** Expansion
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#+begin_src jupyter-python
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%matplotlib inline
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H_exp = SmoothStep(comp_ratio * H_op, 0, t_expansion, 2) + H_op * (1 - comp_ratio)
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tt = np.linspace(0, 1, 1000)
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plt.plot(tt, H_exp.operator_norm(tt))
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@ -76,8 +75,8 @@ Let's lay down some basic functionality.
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#+RESULTS:
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:RESULTS:
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| <matplotlib.lines.Line2D | at | 0x7f4fcc5ac130> |
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[[file:./.ob-jupyter/7d4003fe9d7b0b9dffdf0ac550823de40c40b89d.svg]]
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| <matplotlib.lines.Line2D | at | 0x7fbff84a3340> |
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[[file:./.ob-jupyter/3cc22c46295a7b959768d60bfea0e4c0142e2b43.svg]]
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:END:
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** Hot Thermalization
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@ -90,8 +89,8 @@ Let's lay down some basic functionality.
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#+RESULTS:
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:RESULTS:
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| <matplotlib.lines.Line2D | at | 0x7f4fcbdd7cd0> |
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[[file:./.ob-jupyter/34a09437b2b74d4bbe12efc5c9a3e868bd7ef7e5.svg]]
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| <matplotlib.lines.Line2D | at | 0x7fbff840e1c0> |
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[[file:./.ob-jupyter/98aec1955458432d8e29d21e1675b698b47b50fb.svg]]
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:END:
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** Compression
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@ -102,8 +101,8 @@ Let's lay down some basic functionality.
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#+RESULTS:
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:RESULTS:
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| <matplotlib.lines.Line2D | at | 0x7f4fb41475b0> |
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[[file:./.ob-jupyter/6a32491bdbf52e88d8447cd4e1175d3f8e13a426.svg]]
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| <matplotlib.lines.Line2D | at | 0x7fbff83eeb20> |
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[[file:./.ob-jupyter/8ebe76752707b723232c25684baa8b2b3a0af60e.svg]]
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:END:
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** Cold Thermalization
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@ -116,8 +115,8 @@ Let's lay down some basic functionality.
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#+RESULTS:
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:RESULTS:
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| <matplotlib.lines.Line2D | at | 0x7f4fb4128c40> |
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[[file:./.ob-jupyter/e4d12af68c3403047d6de687c0d98e22db4b026c.svg]]
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| <matplotlib.lines.Line2D | at | 0x7fbfddd4deb0> |
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[[file:./.ob-jupyter/ffdfdc73f371b6485c38c7714e3d6f2075350606.svg]]
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:END:
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** Full Cycle
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@ -141,12 +140,12 @@ Now we turn the system around after each fill-cycle.
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#+end_src
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#+RESULTS:
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[[file:./.ob-jupyter/9fe28aca2667d52381ce08ef48407c6dca2028fb.svg]]
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[[file:./.ob-jupyter/c6fcbae6c5d076e45f48b0d4e003265146c48409.svg]]
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* Model
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#+begin_src jupyter-python :results none
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model = QubitModelMutliBath(
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δ=[.5, .1],
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δ=[.05*4, .01*4],
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ω_c=[1] * 2,
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ω_s=[H_cyc.operator_norm(0) - 1, H_cyc.operator_norm(t_expansion/scale) - 1],
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t=ut.linspace_with_strobe(0, t_max, int(t_max // dt), ω_mod),
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@ -166,20 +165,19 @@ Now we turn the system around after each fill-cycle.
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#+begin_src jupyter-python
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aux.integrate(model, 200)
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aux.integrate(model, 1000)
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#+end_src
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#+RESULTS:
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: [INFO hops.core.integration 7214] Choosing the nonlinear integrator.
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: [INFO hops.core.integration 7214] Using 8 integrators.
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: [INFO hops.core.integration 7214] Some 151 trajectories have to be integrated.
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: [INFO hops.core.integration 7214] Using 1820 hierarchy states.
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: 100% 151/151 [19:43<00:00, 7.84s/it]
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: [INFO hops.core.integration 59572] Choosing the nonlinear integrator.
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: [INFO hops.core.integration 59572] Using 8 integrators.
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: [INFO hops.core.integration 59572] Some 0 trajectories have to be integrated.
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: [INFO hops.core.integration 59572] Using 1820 hierarchy states.
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: 0it [00:00, ?it/s]
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#+begin_src jupyter-python
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%matplotlib inline
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f, a = pu.plot_energy_overview(model, strobe_frequency=ω_mod)
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f, a = pu.plot_energy_overview(model)
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a.plot(model.t, model.H.operator_norm(model.t))
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a.plot(model.t, model.L[0].operator_norm(model.t))
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a.plot(model.t, model.L[1].operator_norm(model.t))
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@ -188,16 +186,28 @@ Now we turn the system around after each fill-cycle.
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#+RESULTS:
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:RESULTS:
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: <matplotlib.legend.Legend at 0x7f4f97d30be0>
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[[file:./.ob-jupyter/dcb781a84fce870deba7b071bc96c4e16b39e164.svg]]
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#+begin_example
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Loading: 100% 8/8 [00:00<00:00, 192.76it/s]
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[INFO root 59572] Writing cache to: results/5e8c8c2b39bd3b8ad75fb0da753ca3989cec2ca758e5b60d542cfb0c39e6009d_12db9adeba3a9d05eee93dba02bb24bbd2bd553fa5e391b4607f3101447ce076_op_exp_task_33_None_7b4b9e9cfa38e0c95465b4087684ba9103d527ed24c440718c98f01d3693d041.npy
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Loading: 100% 16/16 [00:00<00:00, 50.04it/s]
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[INFO root 59572] Writing cache to: results/flow_12db9adeba3a9d05eee93dba02bb24bbd2bd553fa5e391b4607f3101447ce076_flow_worker_33_None_c15d067b912279587be3d3939223173fe5368154834a5a60648fc0a46950f8cf.npy
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Loading: 100% 16/16 [00:00<00:00, 86.42it/s]
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[INFO root 59572] Writing cache to: results/interaction_12db9adeba3a9d05eee93dba02bb24bbd2bd553fa5e391b4607f3101447ce076_interaction_energy_task_33_None_58455bf5ce6e35b96d04516e2a659a907e0c44b588167e00faa3fdf3e226bb11.npy
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Loading: 100% 16/16 [00:00<00:00, 79.44it/s]
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[INFO root 59572] Writing cache to: results/interaction_power_12db9adeba3a9d05eee93dba02bb24bbd2bd553fa5e391b4607f3101447ce076_interaction_energy_task_33_None_58455bf5ce6e35b96d04516e2a659a907e0c44b588167e00faa3fdf3e226bb11.npy
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Loading: 100% 8/8 [00:00<00:00, 245.72it/s]
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[INFO root 59572] Writing cache to: results/143e514d782a0bf3369be86475c494b0df4baa9952cf06bbf576d262f2dd27a4_12db9adeba3a9d05eee93dba02bb24bbd2bd553fa5e391b4607f3101447ce076_op_exp_task_33_None_7b4b9e9cfa38e0c95465b4087684ba9103d527ed24c440718c98f01d3693d041.npy
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#+end_example
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: <matplotlib.legend.Legend at 0x7fbfc42bd580>
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[[file:./.ob-jupyter/229ace105ac0a848d7b968ebdfd79d9fd38dbe9e.svg]]
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:END:
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#+begin_src jupyter-python
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fig, ax = plt.subplots()
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with aux.get_data(model) as data:
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power = model.interaction_power(data).sum_baths()
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power = model.interaction_power(data).for_bath(0)
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power_sys = model.system_power(data).sum_baths()
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energy = model.total_energy_from_power(data).sum_baths()
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energy = model.total_energy_from_power(data)
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pu.plot_with_σ(model.t, power, ax=ax)
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pu.plot_with_σ(model.t, power_sys, ax=ax, label="sys")
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pu.plot_with_σ(model.t, energy, ax=ax)
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@ -207,11 +217,56 @@ Now we turn the system around after each fill-cycle.
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#+RESULTS:
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:RESULTS:
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: <matplotlib.legend.Legend at 0x7f4f974eff10>
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[[file:./.ob-jupyter/c08974c4e215e1890b26e9fc9a31a376b518bfe1.svg]]
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# [goto error]
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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 [97][0m, in [0;36m<cell line: 2>[0;34m()[0m
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[1;32m 1[0m fig, ax [38;5;241m=[39m plt[38;5;241m.[39msubplots()
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[0;32m----> 2[0m [38;5;28;01mwith[39;00m [43maux[49m[38;5;241;43m.[39;49m[43mget_data[49m[43m([49m[43mmodel[49m[43m)[49m [38;5;28;01mas[39;00m data:
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[1;32m 3[0m power [38;5;241m=[39m model[38;5;241m.[39minteraction_power(data)[38;5;241m.[39mfor_bath([38;5;241m0[39m)
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[1;32m 4[0m power_sys [38;5;241m=[39m model[38;5;241m.[39msystem_power(data)[38;5;241m.[39msum_baths()
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File [0;32m~/src/two_qubit_model/hiro_models/model_auxiliary.py:146[0m, in [0;36mget_data[0;34m(model, data_path, read_only, **kwargs)[0m
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[1;32m 135[0m [38;5;28;01mreturn[39;00m HIData(
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[1;32m 136[0m path,
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[1;32m 137[0m hi_key[38;5;241m=[39mmodel[38;5;241m.[39mhops_config,
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[0;32m (...)[0m
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[1;32m 142[0m [38;5;241m*[39m[38;5;241m*[39mkwargs,
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[1;32m 143[0m )
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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 '2676c736eb2bf1d2a1f37e21cda1823a3dc6b3f2cedb82509cfa6231a5232b2f'.
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#+end_example
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[[file:./.ob-jupyter/5285f6b51867d45830747c7d1501193929fe8fd1.svg]]
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:END:
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#+begin_src jupyter-python
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steady_index = 6
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cycle_times, cycle_indices = ut.strobe_times(model.t, ω_mod)
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with aux.get_data(model) as data:
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total_energies = model.total_energy_from_power(data).slice(
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cycle_indices[steady_index:]
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)
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hot_bath_energies = (
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model.bath_energy(data).for_bath(1).slice(cycle_indices[steady_index:])
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)
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e_diff = total_energies.slice(0) - total_energies.slice(-1)
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mean_power = e_diff * (1 / (model.t[-1] - model.t[steady_index]))
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efficiency = e_diff * (
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1 / (hot_bath_energies.slice(0) - hot_bath_energies.slice(-1)).value
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)
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mean_power.value, mean_power.σ, efficiency.value
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#+end_src
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#+RESULTS:
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| -0.011704395396932077 | 8.941753951852788e-05 | -0.6487259446355118 |
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#+begin_src jupyter-python
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with aux.get_data(model) as data:
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ρ = data.rho_t_accum.mean[:]
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@ -224,9 +279,9 @@ Now we turn the system around after each fill-cycle.
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#+RESULTS:
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:RESULTS:
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: /nix/store/yf7vf1pic6xfwr035xywcczjm08psvpa-python3-3.9.13-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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: /nix/store/dbz51ji4p5pcppra5h7yqhwcrbqgq40v-python3-3.9.13-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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[[file:./.ob-jupyter/5dec5cced33bb9d2ede9b8805dd77daf9dfc5a24.svg]]
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[[file:./.ob-jupyter/9b6dbfd5d863d78fc20ce574cf7e282bc8341b0c.svg]]
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:END:
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@ -244,9 +299,9 @@ Now we turn the system around after each fill-cycle.
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#+RESULTS:
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:RESULTS:
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: /nix/store/yf7vf1pic6xfwr035xywcczjm08psvpa-python3-3.9.13-env/lib/python3.9/site-packages/qutip/bloch.py:639: UserWarning: There are no gridspecs with layoutgrids. Possibly did not call parent GridSpec with the "figure" keyword
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: /nix/store/dbz51ji4p5pcppra5h7yqhwcrbqgq40v-python3-3.9.13-env/lib/python3.9/site-packages/qutip/bloch.py:639: UserWarning: There are no gridspecs with layoutgrids. Possibly did not call parent GridSpec with the "figure" keyword
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: self.fig.canvas.draw()
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: /nix/store/yf7vf1pic6xfwr035xywcczjm08psvpa-python3-3.9.13-env/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: There are no gridspecs with layoutgrids. Possibly did not call parent GridSpec with the "figure" keyword
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: /nix/store/dbz51ji4p5pcppra5h7yqhwcrbqgq40v-python3-3.9.13-env/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: There are no gridspecs with layoutgrids. Possibly did not call parent GridSpec with the "figure" keyword
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: fig.canvas.print_figure(bytes_io, **kw)
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[[file:./.ob-jupyter/b356a7697a8328d1eb85611295a8b5bfbff2e66b.svg]]
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[[file:./.ob-jupyter/7806ea3a984dc21f51c7f09f8720ae8e361465a5.svg]]
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:END:
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@ -25,7 +25,7 @@ print(H_op, L_op, sep="\n")
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t_compression = .1
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t_expansion = .1
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comp_ratio = .5
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comp_ratio = .25
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hot_switch_ratio = .8
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cold_switch_ratio = .8
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@ -35,7 +35,6 @@ t_cold = (1 - t_expansion - t_compression) / 2
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t_cold_switch = t_cold * cold_switch_ratio / 2
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t = np.linspace(0, 10, 1000)
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%matplotlib inline
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H_exp = SmoothStep(comp_ratio * H_op, 0, t_expansion, 2) + H_op * (1 - comp_ratio)
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tt = np.linspace(0, 1, 1000)
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plt.plot(tt, H_exp.operator_norm(tt))
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@ -70,7 +69,7 @@ t_max = t_cycle * periods
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dt = .01
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model = QubitModelMutliBath(
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δ=[.5, .1],
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δ=[.05*4, .01*4],
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ω_c=[1] * 2,
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ω_s=[H_cyc.operator_norm(0) - 1, H_cyc.operator_norm(t_expansion/scale) - 1],
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t=ut.linspace_with_strobe(0, t_max, int(t_max // dt), ω_mod),
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@ -87,10 +86,9 @@ model = QubitModelMutliBath(
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therm_methods=["tanhsinh", "fft"],
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)
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aux.integrate(model, 100)
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aux.integrate(model, 1000)
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%matplotlib inline
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f, a = pu.plot_energy_overview(model, strobe_frequency=ω_mod)
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f, a = pu.plot_energy_overview(model)
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a.plot(model.t, model.H.operator_norm(model.t))
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a.plot(model.t, model.L[0].operator_norm(model.t))
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a.plot(model.t, model.L[1].operator_norm(model.t))
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@ -98,9 +96,9 @@ a.legend()
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fig, ax = plt.subplots()
|
||||
with aux.get_data(model) as data:
|
||||
power = model.interaction_power(data).sum_baths()
|
||||
power = model.interaction_power(data).for_bath(0)
|
||||
power_sys = model.system_power(data).sum_baths()
|
||||
energy = model.total_energy_from_power(data).sum_baths()
|
||||
energy = model.total_energy_from_power(data)
|
||||
pu.plot_with_σ(model.t, power, ax=ax)
|
||||
pu.plot_with_σ(model.t, power_sys, ax=ax, label="sys")
|
||||
pu.plot_with_σ(model.t, energy, ax=ax)
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue