tangle anti zeno

python/energy_flow_proper/10_antizeno_engine/10_first_anti_zeno.py

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+import figsaver as fs
+from hiro_models.one_qubit_model import QubitModelMutliBath, StocProcTolerances
+import hiro_models.model_auxiliary as aux
+from hops.util.utilities import relative_entropy, relative_entropy_single, entropy, trace_distance
+from hopsflow.util import EnsembleValue
+import numpy as np
+import qutip as qt
+import scipy
+import utilities as ut
+from hops.util.dynamic_matrix import SmoothStep, Periodic, Harmonic, ConstantMatrix, Piecewise
+
+import ray
+ray.shutdown()
+ray.init(address="auto")
+
+from hops.util.logging_setup import logging_setup
+import logging
+logging_setup(logging.INFO, show_stocproc=False)
+
+Δ = 7
+ω_c = 2
+τ_bath = 1 / ω_c
+τ_mod = 2 * np.pi / Δ
+
+cycle_scale = 15  # 12
+cycles = int(np.around(τ_bath / τ_mod * cycle_scale))
+τ_c = cycles * τ_mod
+τ_off = τ_c * 5
+
+detune = 0
+
+γ = 0.1 / 2
+
+ω_0 = 10
+H_mat = 1 / 2 * (qt.sigmaz().full()) * ω_0
+Δ_switch = τ_mod * 1
+n = 10
+t_max = n * (τ_c + τ_off)
+
+H = ConstantMatrix(H_mat) + γ * Δ * (
+    Harmonic(H_mat, Δ)
+    @ (
+        Periodic(
+            ConstantMatrix(np.eye(2))
+            - SmoothStep(np.eye(2), τ_c, τ_c + Δ_switch, 2)
+            + SmoothStep(np.eye(2), τ_c + τ_off - Δ_switch, τ_c + τ_off, 2),
+            τ_c + τ_off,
+        )
+    )
+)
+
+
+L_mat = qt.sigmax().full()
+
+
+L = Periodic(
+    ConstantMatrix(L_mat)
+    - SmoothStep(L_mat, τ_c, τ_c + Δ_switch, 2)
+    + SmoothStep(L_mat, τ_c + τ_off - Δ_switch, τ_c + τ_off, 2),
+    τ_c + τ_off,
+)
+
+
+model = QubitModelMutliBath(
+    δ=[1, 1],
+    ω_c=[ω_c, ω_c],
+    ω_s=[ω_0 - Δ - ω_c - detune, ω_0 + Δ - ω_c + detune],
+    t=ut.linspace_with_strobe(0, t_max, n * 1000, Δ),
+    ψ_0=(0 * qt.basis([2], [0]) + qt.basis([2], [1])),
+    description=f"Building the anti-zento-engine",
+    k_max=4,
+    bcf_terms=[5, 5],
+    truncation_scheme="simplex",
+    driving_process_tolerances=[StocProcTolerances(1e-3, 1e-3)] * 2,
+    thermal_process_tolerances=[StocProcTolerances(1e-3, 1e-3)] * 2,
+    T=[2, 12],
+    L=[L] * 2,
+    H=H,
+    therm_methods=["tanhsinh", "fft"],
+)
+
+
+τ_mod, τ_c, τ_bath, cycles, model.ω_s, 1 / τ_c, model.T, ω_0 - Δ
+
+
+
+def thermal_bcf(t):
+    return model.bcf(1)(t) + 2 * (model.thermal_correlations(1)(t).real)
+plt.plot(model.t, np.abs(thermal_bcf(model.t))/np.abs(thermal_bcf(0)))
+plt.plot(model.t, model.L[0].operator_norm(model.t))
+plt.plot(model.t, model.H.operator_norm(model.t) - 5)
+plt.plot(model.t, np.exp(- model.t * np.min(np.array(model.bcf_coefficients()[1][0]).real)))
+
+ωs = np.linspace(0, 20, 1000)
+plt.plot(ωs, model.spectral_density(0)(ωs))
+plt.plot(ωs, model.spectral_density(1)(ωs))
+
+aux.integrate(model, 50)
+
+_, ax = fs.plot_energy_overview(model, markersize=1)
+# with aux.get_data(model) as data:
+#     fs.plot_with_σ(model.t, model.total_energy(data), ax=ax)
+
+ax.legend()
+
+with aux.get_data(model) as data:
+    fs.plot_with_σ(
+        model.t,
+        EnsembleValue(
+            (data.rho_t_accum.mean[:, 0, 0], data.rho_t_accum.ensemble_std[:, 0, 0])
+
+        ),
+    )
+
+    ρ_ee = data.rho_t_accum.mean[:, 0, 0].real
+
+plt.plot(model.t, ρ_ee)
+
+from statsmodels.nonparametric.smoothers_lowess import lowess
+filtered = lowess(ρ_ee, model.t, is_sorted=True, return_sorted=False, frac=0.1, it=0)
+plt.plot(model.t, filtered)
+
+with aux.get_data(model) as data:
+  _, ax = plt.subplots()
+  #fs.plot_with_σ(model.t, model.bath_energy(data), bath=1, ax=ax)
+  fs.plot_with_σ(model.t, model.total_energy(data), ax=ax)
+  #fs.plot_with_σ(model.t, model.system_energy(data), ax=ax)
+
+  #fs.plot_with_σ(model.t, model.system_energy(data) + model.bath_energy(data).sum_baths() , ax=ax)
+
+with aux.get_data(model) as data:
+  _, ax = plt.subplots()
+  #fs.plot_with_σ(model.t, model.bath_energy(data), bath=1, ax=ax)
+  fs.plot_diff_vs_sigma(model.t, model.total_energy(data, every=50)[:-1], model.total_energy(data, every=50)[-1], ax=ax)
+  ax.legend()
+
+mean_norm = np.zeros_like(model.t)
+with aux.get_data(model) as data:
+    for i in range(data.samples):
+        aux_state = data.aux_states[i, :]
+        # plt.plot(model.t, np.linalg.norm(aux_state, axis=1))
+
+        mean_norm += np.linalg.norm(aux_state, axis=1)
+
+    mean_norm /= data.samples
+
+plt.plot(model.t, mean_norm)