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https://github.com/vale981/master-thesis
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a working otto cycle
This commit is contained in:
parent
097586299b
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6 changed files with 293 additions and 111 deletions
1
.gitignore
vendored
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.gitignore
vendored
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@ -506,3 +506,4 @@ results/**.npy
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/python/energy_flow_proper/11_new_ho_comparison/ho_data_local/
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/python/energy_flow_proper/11_new_ho_comparison/local_ho_data/
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/python/energy_flow_proper/10_antizeno_engine/taurus/
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/python/energy_flow_proper/09_dynamic_two_bath_one_qubit/taurus/
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@ -1,2 +1,2 @@
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use_flake
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use_flake --impure
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eval "$shellHook"
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@ -12,6 +12,7 @@ with the markovian result.
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import qutip as qt
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import utilities as ut
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import stocproc
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import matplotlib.pyplot as plt
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#+end_src
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Init ray and silence stocproc.
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@ -23,7 +24,7 @@ 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_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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: 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_16-33-19_035369_76479/sockets/plasma_store', 'raylet_socket_name': '/tmp/ray/session_2022-08-15_16-33-19_035369_76479/sockets/raylet', 'webui_url': '', 'session_dir': '/tmp/ray/session_2022-08-15_16-33-19_035369_76479', 'metrics_export_port': 56879, 'gcs_address': '192.168.100.170:64999', 'address': '192.168.100.170:64999', 'node_id': 'cd3c35a56063d205e53b29934473b89b3baea526868d0ff8e806b08e'})
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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 +56,7 @@ 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 = .25
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comp_ratio = .5
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hot_switch_ratio = .8
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cold_switch_ratio = .8
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@ -75,8 +76,8 @@ 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 | 0x7fbff84a3340> |
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[[file:./.ob-jupyter/3cc22c46295a7b959768d60bfea0e4c0142e2b43.svg]]
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| <matplotlib.lines.Line2D | at | 0x7f7678510df0> |
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[[file:./.ob-jupyter/8326a30a0b67dac1fcbfa726d3677ad081f50681.svg]]
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:END:
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** Hot Thermalization
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@ -89,8 +90,8 @@ 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 | 0x7fbff840e1c0> |
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[[file:./.ob-jupyter/98aec1955458432d8e29d21e1675b698b47b50fb.svg]]
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| <matplotlib.lines.Line2D | at | 0x7f7669dd3b50> |
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[[file:./.ob-jupyter/beef0051823ad8d69a03749d66188767c06f00d6.svg]]
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:END:
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** Compression
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@ -101,8 +102,8 @@ 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 | 0x7fbff83eeb20> |
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[[file:./.ob-jupyter/8ebe76752707b723232c25684baa8b2b3a0af60e.svg]]
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| <matplotlib.lines.Line2D | at | 0x7f7669d4e100> |
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[[file:./.ob-jupyter/2c1c45c42776db17721492efacd6f4856f8fe3b3.svg]]
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:END:
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** Cold Thermalization
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@ -115,8 +116,8 @@ 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 | 0x7fbfddd4deb0> |
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[[file:./.ob-jupyter/ffdfdc73f371b6485c38c7714e3d6f2075350606.svg]]
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| <matplotlib.lines.Line2D | at | 0x7f7669d33610> |
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[[file:./.ob-jupyter/a425435a0d34373dd92cf2a415ceec18a1fd69af.svg]]
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:END:
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** Full Cycle
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@ -125,29 +126,73 @@ Now we turn the system around after each fill-cycle.
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H_cyc = Periodic(Piecewise([H_exp, H_comp], [0, t_expansion, 1]), 1)
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L = [Periodic(L_i, 1) for L_i in (L_cold, L_hot)]
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period = 1
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scale = .1
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scale = .05
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H_cyc, *L = [ScaleTime(op, scale) for op in [H_cyc, *L]]
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ω_mod = 2*np.pi / (period) * scale
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plt.plot(t, H_cyc.operator_norm(t))
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plt.plot(t, L[0].operator_norm(t))
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plt.plot(t, L[1].operator_norm(t))
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ω_mod
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t_cycle = period/scale
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periods = 20
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periods = 10
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t_max = t_cycle * periods
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dt = .01
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dt = .1
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t = np.linspace(0, t_cycle, 1000)
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plt.plot(t, H_cyc.operator_norm(t), label=r"$||H_\mathrm{S}||$")
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plt.plot(t, L[0].operator_norm(t), label=r"$||L_\mathrm{c}||$")
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plt.plot(t, L[1].operator_norm(t), label=r"$||L_\mathrm{h}||$")
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plt.xlabel(r"$\tau$")
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plt.legend()
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fs.export_fig("modulation")
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#+end_src
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#+RESULTS:
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[[file:./.ob-jupyter/c6fcbae6c5d076e45f48b0d4e003265146c48409.svg]]
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[[file:./.ob-jupyter/2b79808a3127f0a93ce6cbde00ae4888c83b2696.svg]]
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** Shifts
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#+begin_src jupyter-python
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from scipy.optimize import minimize_scalar
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ref_model = QubitModelMutliBath(
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ω_c=[1] * 2,
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T=[1, 20],
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)
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shifts = []
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scales = []
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for i, t_exp in enumerate([0, t_expansion / scale]):
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ω_exp = H_cyc.operator_norm(i)
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def objective(ω_s):
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ref_model.ω_s[i] = ω_s
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return -ref_model.full_thermal_spectral_density(i)(ω_exp)
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res = minimize_scalar(
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objective, 1, method="bounded", bounds=(0.01, ω_exp), options=dict(maxiter=100)
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)
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shifts.append(res.x)
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scales.append(-res.fun)
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ω = np.linspace(0.1, 10, 1000)
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plt.plot(ω, ref_model.full_thermal_spectral_density(i)(ω) / -res.fun)
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print(shifts, scales)
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#+end_src
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#+RESULTS:
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:RESULTS:
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: [0.010005778765750251, 0.5000003902186526] [0.3656483223378975, 3.198956858189882]
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[[file:./.ob-jupyter/97d3960ba8288d117b1bf5774e57ac8bb1500b83.svg]]
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:END:
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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=[.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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δ=list(1/np.array(scales) * .1/2),#[.05*4, .01*4],
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ω_c=ref_model.ω_c,
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ω_s=shifts,#[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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ψ_0=(qt.basis([2], [0])*0 + qt.basis([2], [1])*1),
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description=f"A simple otto cycle.",
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@ -156,95 +201,60 @@ Now we turn the system around after each fill-cycle.
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truncation_scheme="simplex",
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driving_process_tolerances=[StocProcTolerances(1e-3, 1e-3)] * 2,
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thermal_process_tolerances=[StocProcTolerances(1e-3, 1e-3)] * 2,
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T = [1, 20],
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T = ref_model.T, #= [1, 20],
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L = L,
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H = H_cyc,
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therm_methods=["tanhsinh", "fft"],
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therm_methods=["fft", "fft"],
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)
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#+end_src
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#+begin_src jupyter-python
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aux.integrate(model, 1000)
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aux.integrate(model, 10000)
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#+end_src
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#+RESULTS:
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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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: [INFO hops.core.integration 76479] Choosing the nonlinear integrator.
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: [INFO hops.core.integration 76479] Using 8 integrators.
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: [INFO hops.core.integration 76479] Some 30 trajectories have to be integrated.
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: [INFO hops.core.integration 76479] Using 1820 hierarchy states.
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: 100% 30/30 [00:22<00:00, 1.32it/s]
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#+begin_src jupyter-python
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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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f, a = pu.plot_energy_overview(model, strobe_frequency=ω_mod)
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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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ax.set_xlabel(r"$\tau$")
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a.legend()
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#fs.export_fig("energy_strobe", y_scaling=.6)
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#+end_src
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#+RESULTS:
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:RESULTS:
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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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[[file:./.ob-jupyter/5d6c6959a02462528cdb3d541f8dd5ecf303f362.svg]]
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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).for_bath(0)
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power = model.interaction_power(data).sum_baths()
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power_sys = model.system_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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pu.plot_with_σ(model.t, power, ax=ax, label="Interaction")
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pu.plot_with_σ(model.t, power_sys, ax=ax, label="System")
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ax.legend()
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ax.set_title("Power")
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ax.set_xlabel(r"$\tau$")
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fs.export_fig("power", y_scaling=.4)
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#+end_src
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#+RESULTS:
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:RESULTS:
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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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[[file:./.ob-jupyter/4938909f0a3699e7c9912ab201d2f04199b355e7.svg]]
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#+begin_src jupyter-python
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steady_index = 6
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steady_index = 3
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cycle_times, cycle_indices = ut.strobe_times(model.t, ω_mod)
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@ -257,7 +267,7 @@ Now we turn the system around after each fill-cycle.
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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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mean_power = e_diff * (1 / (model.t[-1] - cycle_times[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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@ -265,13 +275,13 @@ 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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| -0.011704395396932077 | 8.941753951852788e-05 | -0.6487259446355118 |
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| 0.0019806021074448648 | 2.3085106663538818e-05 | 0.1975630167587971 |
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|
||||
#+begin_src jupyter-python
|
||||
with aux.get_data(model) as data:
|
||||
ρ = data.rho_t_accum.mean[:]
|
||||
σ_ρ = data.rho_t_accum.ensemble_std[:]
|
||||
plt.plot(model.t, ρ[:, 1, 1])
|
||||
plt.plot(model.t, ρ[:, 0, 0])
|
||||
xs = np.einsum("tij,ji->t", ρ, qt.sigmax().full())
|
||||
ys = np.einsum("tij,ji->t", ρ, qt.sigmay().full())
|
||||
zs = np.einsum("tij,ji->t", ρ, qt.sigmaz().full())
|
||||
|
|
@ -279,9 +289,9 @@ Now we turn the system around after each fill-cycle.
|
|||
|
||||
#+RESULTS:
|
||||
:RESULTS:
|
||||
: /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
|
||||
: /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
|
||||
: return np.asarray(x, float)
|
||||
[[file:./.ob-jupyter/9b6dbfd5d863d78fc20ce574cf7e282bc8341b0c.svg]]
|
||||
[[file:./.ob-jupyter/69c7da5a4fa9adc0fb49b0ff097ea6fbc28c58ca.svg]]
|
||||
:END:
|
||||
|
||||
|
||||
|
|
@ -299,9 +309,9 @@ Now we turn the system around after each fill-cycle.
|
|||
|
||||
#+RESULTS:
|
||||
:RESULTS:
|
||||
: /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
|
||||
: /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
|
||||
: self.fig.canvas.draw()
|
||||
: /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
|
||||
: /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
|
||||
: fig.canvas.print_figure(bytes_io, **kw)
|
||||
[[file:./.ob-jupyter/7806ea3a984dc21f51c7f09f8720ae8e361465a5.svg]]
|
||||
[[file:./.ob-jupyter/633b45a276b59c015a575e42b611b52283311ebe.svg]]
|
||||
:END:
|
||||
|
|
|
|||
7
python/energy_flow_proper/09_dynamic_two_bath_one_qubit/mount_taurus.sh
Executable file
7
python/energy_flow_proper/09_dynamic_two_bath_one_qubit/mount_taurus.sh
Executable file
|
|
@ -0,0 +1,7 @@
|
|||
#!/usr/bin/env bash
|
||||
sshfs -oIdentityFile=~/.ssh/id_ed25519_taurus s8896854@taurusexport.hrsk.tu-dresden.de:/lustre/ssd/ws/s8896854-m_11/project/python/energy_flow_proper/09_dynamic_two_bath_one_qubit/.data .data
|
||||
sshfs -oIdentityFile=~/.ssh/id_ed25519_taurus s8896854@taurusexport.hrsk.tu-dresden.de:/lustre/ssd/ws/s8896854-m_11/project/python/energy_flow_proper/09_dynamic_two_bath_one_qubit/results results
|
||||
|
||||
|
||||
|
||||
sshfs -oIdentityFile=~/.ssh/id_ed25519_taurus s8896854@taurusexport.hrsk.tu-dresden.de:/lustre/ssd/ws/s8896854-m_11/project/python/energy_flow_proper/09_dynamic_two_bath_one_qubit/ taurus
|
||||
|
|
@ -0,0 +1,125 @@
|
|||
import figsaver as fs
|
||||
import plot_utils as pu
|
||||
from hiro_models.one_qubit_model import QubitModelMutliBath, StocProcTolerances
|
||||
import hiro_models.model_auxiliary as aux
|
||||
import numpy as np
|
||||
import qutip as qt
|
||||
import utilities as ut
|
||||
import stocproc
|
||||
|
||||
import ray
|
||||
ray.shutdown()
|
||||
#ray.init(address='auto')
|
||||
ray.init()
|
||||
|
||||
from hops.util.logging_setup import logging_setup
|
||||
import logging
|
||||
logging_setup(logging.INFO)
|
||||
|
||||
from hops.util.dynamic_matrix import SmoothStep, Periodic, ConstantMatrix, ScaleTime, Shift, Piecewise
|
||||
|
||||
H_op = 2*(1/2 * (qt.sigmaz() + qt.identity(2))).full()
|
||||
L_op = (1/2 * qt.sigmax()).full()
|
||||
L_op_2 = (1/2 * qt.sigmax()).full()
|
||||
print(H_op, L_op, sep="\n")
|
||||
|
||||
t_compression = .1
|
||||
t_expansion = .1
|
||||
comp_ratio = .5
|
||||
hot_switch_ratio = .8
|
||||
cold_switch_ratio = .8
|
||||
|
||||
t_hot = (1 - t_expansion - t_compression) / 2
|
||||
t_hot_switch = t_hot * hot_switch_ratio / 2
|
||||
t_cold = (1 - t_expansion - t_compression) / 2
|
||||
t_cold_switch = t_cold * cold_switch_ratio / 2
|
||||
t = np.linspace(0, 10, 1000)
|
||||
|
||||
%matplotlib inline
|
||||
H_exp = SmoothStep(comp_ratio * H_op, 0, t_expansion, 2) + H_op * (1 - comp_ratio)
|
||||
tt = np.linspace(0, 1, 1000)
|
||||
plt.plot(tt, H_exp.operator_norm(tt))
|
||||
|
||||
L_hot = SmoothStep(L_op, t_expansion, t_expansion + t_hot_switch, 2) - SmoothStep(
|
||||
L_op, t_expansion + t_hot - t_hot_switch, t_expansion + t_hot, 2
|
||||
)
|
||||
plt.plot(tt, L_hot.operator_norm(tt))
|
||||
|
||||
H_comp = ConstantMatrix(H_op) - SmoothStep(comp_ratio * H_op, t_expansion + t_hot, t_expansion + t_hot + t_compression, 2)
|
||||
plt.plot(tt, H_comp.operator_norm(tt))
|
||||
|
||||
L_cold = SmoothStep(L_op_2, t_expansion + t_hot + t_compression, t_expansion + t_hot + t_compression + t_cold_switch, 3) - SmoothStep(
|
||||
L_op_2, t_expansion + t_hot + t_compression + t_cold - t_cold_switch, 1, 2
|
||||
)
|
||||
plt.plot(tt, L_cold.operator_norm(tt))
|
||||
|
||||
H_cyc = Periodic(Piecewise([H_exp, H_comp], [0, t_expansion, 1]), 1)
|
||||
L = [Periodic(L_i, 1) for L_i in (L_cold, L_hot)]
|
||||
period = 1
|
||||
scale = .1
|
||||
H_cyc, *L = [ScaleTime(op, scale) for op in [H_cyc, *L]]
|
||||
|
||||
ω_mod = 2*np.pi / (period) * scale
|
||||
plt.plot(t, H_cyc.operator_norm(t))
|
||||
plt.plot(t, L[0].operator_norm(t))
|
||||
plt.plot(t, L[1].operator_norm(t))
|
||||
ω_mod
|
||||
t_cycle = period/scale
|
||||
periods = 20
|
||||
t_max = t_cycle * periods
|
||||
dt = .01
|
||||
|
||||
model = QubitModelMutliBath(
|
||||
δ=[.5, .1],
|
||||
ω_c=[1] * 2,
|
||||
ω_s=[H_cyc.operator_norm(0) - 1, H_cyc.operator_norm(t_expansion/scale) - 1],
|
||||
t=ut.linspace_with_strobe(0, t_max, int(t_max // dt), ω_mod),
|
||||
ψ_0=(qt.basis([2], [0])*0 + qt.basis([2], [1])*1),
|
||||
description=f"A simple otto cycle.",
|
||||
k_max=4,
|
||||
bcf_terms=[6]*2,
|
||||
truncation_scheme="simplex",
|
||||
driving_process_tolerances=[StocProcTolerances(1e-3, 1e-3)] * 2,
|
||||
thermal_process_tolerances=[StocProcTolerances(1e-3, 1e-3)] * 2,
|
||||
T = [0, 20],
|
||||
L = L,
|
||||
H = H_cyc,
|
||||
therm_methods=["tanhsinh", "fft"],
|
||||
)
|
||||
|
||||
aux.integrate(model, 200)
|
||||
|
||||
%matplotlib inline
|
||||
f, a = pu.plot_energy_overview(model)
|
||||
a.plot(model.t, model.H.operator_norm(model.t))
|
||||
a.plot(model.t, model.L[0].operator_norm(model.t))
|
||||
a.plot(model.t, model.L[1].operator_norm(model.t))
|
||||
a.legend()
|
||||
|
||||
fig, ax = plt.subplots()
|
||||
with aux.get_data(model) as data:
|
||||
power = model.interaction_power(data).sum_baths()
|
||||
power_sys = model.system_power(data).sum_baths()
|
||||
energy = model.total_energy_from_power(data).sum_baths()
|
||||
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)
|
||||
|
||||
ax.legend()
|
||||
|
||||
with aux.get_data(model) as data:
|
||||
ρ = data.rho_t_accum.mean[:]
|
||||
σ_ρ = data.rho_t_accum.ensemble_std[:]
|
||||
plt.plot(model.t, ρ[:, 1, 1])
|
||||
xs = np.einsum("tij,ji->t", ρ, qt.sigmax().full())
|
||||
ys = np.einsum("tij,ji->t", ρ, qt.sigmay().full())
|
||||
zs = np.einsum("tij,ji->t", ρ, qt.sigmaz().full())
|
||||
|
||||
b = qt.Bloch()
|
||||
b.add_points([xs, ys, zs])
|
||||
b.view = [20, 20]
|
||||
b.point_size = [0.1]
|
||||
b.sphere_alpha = 0.1
|
||||
b.size = [20, 20]
|
||||
b.render()
|
||||
b.fig
|
||||
|
|
@ -6,6 +6,7 @@ import numpy as np
|
|||
import qutip as qt
|
||||
import utilities as ut
|
||||
import stocproc
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
import ray
|
||||
ray.shutdown()
|
||||
|
|
@ -25,7 +26,7 @@ print(H_op, L_op, sep="\n")
|
|||
|
||||
t_compression = .1
|
||||
t_expansion = .1
|
||||
comp_ratio = .25
|
||||
comp_ratio = .5
|
||||
hot_switch_ratio = .8
|
||||
cold_switch_ratio = .8
|
||||
|
||||
|
|
@ -55,23 +56,57 @@ plt.plot(tt, L_cold.operator_norm(tt))
|
|||
H_cyc = Periodic(Piecewise([H_exp, H_comp], [0, t_expansion, 1]), 1)
|
||||
L = [Periodic(L_i, 1) for L_i in (L_cold, L_hot)]
|
||||
period = 1
|
||||
scale = .1
|
||||
scale = .05
|
||||
H_cyc, *L = [ScaleTime(op, scale) for op in [H_cyc, *L]]
|
||||
|
||||
ω_mod = 2*np.pi / (period) * scale
|
||||
plt.plot(t, H_cyc.operator_norm(t))
|
||||
plt.plot(t, L[0].operator_norm(t))
|
||||
plt.plot(t, L[1].operator_norm(t))
|
||||
ω_mod
|
||||
t_cycle = period/scale
|
||||
periods = 20
|
||||
periods = 10
|
||||
t_max = t_cycle * periods
|
||||
dt = .01
|
||||
dt = .1
|
||||
|
||||
t = np.linspace(0, t_cycle, 1000)
|
||||
|
||||
plt.plot(t, H_cyc.operator_norm(t), label=r"$||H_\mathrm{S}||$")
|
||||
plt.plot(t, L[0].operator_norm(t), label=r"$||L_\mathrm{c}||$")
|
||||
plt.plot(t, L[1].operator_norm(t), label=r"$||L_\mathrm{h}||$")
|
||||
plt.xlabel(r"$\tau$")
|
||||
plt.legend()
|
||||
fs.export_fig("modulation")
|
||||
|
||||
from scipy.optimize import minimize_scalar
|
||||
|
||||
ref_model = QubitModelMutliBath(
|
||||
ω_c=[1] * 2,
|
||||
T=[1, 20],
|
||||
)
|
||||
|
||||
shifts = []
|
||||
scales = []
|
||||
|
||||
for i, t_exp in enumerate([0, t_expansion / scale]):
|
||||
ω_exp = H_cyc.operator_norm(i)
|
||||
|
||||
def objective(ω_s):
|
||||
ref_model.ω_s[i] = ω_s
|
||||
|
||||
return -ref_model.full_thermal_spectral_density(i)(ω_exp)
|
||||
|
||||
res = minimize_scalar(
|
||||
objective, 1, method="bounded", bounds=(0.01, ω_exp), options=dict(maxiter=100)
|
||||
)
|
||||
shifts.append(res.x)
|
||||
scales.append(-res.fun)
|
||||
ω = np.linspace(0.1, 10, 1000)
|
||||
plt.plot(ω, ref_model.full_thermal_spectral_density(i)(ω) / -res.fun)
|
||||
|
||||
print(shifts, scales)
|
||||
|
||||
model = QubitModelMutliBath(
|
||||
δ=[.05*4, .01*4],
|
||||
ω_c=[1] * 2,
|
||||
ω_s=[H_cyc.operator_norm(0) - 1, H_cyc.operator_norm(t_expansion/scale) - 1],
|
||||
δ=list(1/np.array(scales) * .1/2),#[.05*4, .01*4],
|
||||
ω_c=ref_model.ω_c,
|
||||
ω_s=shifts,#[H_cyc.operator_norm(0) - 1, H_cyc.operator_norm(t_expansion/scale) - 1],
|
||||
t=ut.linspace_with_strobe(0, t_max, int(t_max // dt), ω_mod),
|
||||
ψ_0=(qt.basis([2], [0])*0 + qt.basis([2], [1])*1),
|
||||
description=f"A simple otto cycle.",
|
||||
|
|
@ -80,32 +115,36 @@ model = QubitModelMutliBath(
|
|||
truncation_scheme="simplex",
|
||||
driving_process_tolerances=[StocProcTolerances(1e-3, 1e-3)] * 2,
|
||||
thermal_process_tolerances=[StocProcTolerances(1e-3, 1e-3)] * 2,
|
||||
T = [1, 20],
|
||||
T = ref_model.T, #= [1, 20],
|
||||
L = L,
|
||||
H = H_cyc,
|
||||
therm_methods=["tanhsinh", "fft"],
|
||||
therm_methods=["fft", "fft"],
|
||||
)
|
||||
|
||||
aux.integrate(model, 1000)
|
||||
aux.integrate(model, 10000)
|
||||
|
||||
f, a = pu.plot_energy_overview(model)
|
||||
a.plot(model.t, model.H.operator_norm(model.t))
|
||||
a.plot(model.t, model.L[0].operator_norm(model.t))
|
||||
a.plot(model.t, model.L[1].operator_norm(model.t))
|
||||
f, a = pu.plot_energy_overview(model, strobe_frequency=ω_mod)
|
||||
# a.plot(model.t, model.H.operator_norm(model.t))
|
||||
# a.plot(model.t, model.L[0].operator_norm(model.t))
|
||||
# a.plot(model.t, model.L[1].operator_norm(model.t))
|
||||
ax.set_xlabel(r"$\tau$")
|
||||
a.legend()
|
||||
#fs.export_fig("energy_strobe", y_scaling=.6)
|
||||
|
||||
fig, ax = plt.subplots()
|
||||
with aux.get_data(model) as data:
|
||||
power = model.interaction_power(data).for_bath(0)
|
||||
power = model.interaction_power(data).sum_baths()
|
||||
power_sys = model.system_power(data).sum_baths()
|
||||
energy = model.total_energy_from_power(data)
|
||||
pu.plot_with_σ(model.t, power, ax=ax)
|
||||
pu.plot_with_σ(model.t, power_sys, ax=ax, label="sys")
|
||||
pu.plot_with_σ(model.t, energy, ax=ax)
|
||||
pu.plot_with_σ(model.t, power, ax=ax, label="Interaction")
|
||||
pu.plot_with_σ(model.t, power_sys, ax=ax, label="System")
|
||||
|
||||
ax.legend()
|
||||
ax.set_title("Power")
|
||||
ax.set_xlabel(r"$\tau$")
|
||||
fs.export_fig("power", y_scaling=.4)
|
||||
|
||||
steady_index = 6
|
||||
steady_index = 3
|
||||
cycle_times, cycle_indices = ut.strobe_times(model.t, ω_mod)
|
||||
|
||||
|
||||
|
|
@ -118,7 +157,7 @@ with aux.get_data(model) as data:
|
|||
)
|
||||
|
||||
e_diff = total_energies.slice(0) - total_energies.slice(-1)
|
||||
mean_power = e_diff * (1 / (model.t[-1] - model.t[steady_index]))
|
||||
mean_power = e_diff * (1 / (model.t[-1] - cycle_times[steady_index]))
|
||||
efficiency = e_diff * (
|
||||
1 / (hot_bath_energies.slice(0) - hot_bath_energies.slice(-1)).value
|
||||
)
|
||||
|
|
@ -127,7 +166,7 @@ mean_power.value, mean_power.σ, efficiency.value
|
|||
with aux.get_data(model) as data:
|
||||
ρ = data.rho_t_accum.mean[:]
|
||||
σ_ρ = data.rho_t_accum.ensemble_std[:]
|
||||
plt.plot(model.t, ρ[:, 1, 1])
|
||||
plt.plot(model.t, ρ[:, 0, 0])
|
||||
xs = np.einsum("tij,ji->t", ρ, qt.sigmax().full())
|
||||
ys = np.einsum("tij,ji->t", ρ, qt.sigmay().full())
|
||||
zs = np.einsum("tij,ji->t", ρ, qt.sigmaz().full())
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue