mirror of
https://github.com/vale981/HOPSFlow-Paper
synced 2025-03-06 10:11:39 -05:00
80 lines
2.1 KiB
Python
80 lines
2.1 KiB
Python
import figsaver as fs
|
|
import plot_utils as pu
|
|
from hiro_models.one_qubit_model import StocProcTolerances
|
|
from hiro_models.otto_cycle import OttoEngine
|
|
import hiro_models.model_auxiliary as aux
|
|
import numpy as np
|
|
import qutip as qt
|
|
import utilities as ut
|
|
import stocproc
|
|
import matplotlib.pyplot as plt
|
|
import otto_utilities as ot
|
|
|
|
import ray
|
|
ray.shutdown()
|
|
|
|
#ray.init(address='auto')
|
|
ray.init()
|
|
from hops.util.logging_setup import logging_setup
|
|
import logging
|
|
logging_setup(logging.INFO)
|
|
plt.rcParams['figure.figsize'] = (12,4)
|
|
|
|
def timings(τ_c, τ_i, percent_overlap=0):
|
|
τ_cI = τ_c * (1-percent_overlap)
|
|
|
|
τ_thI = (1 - 2 * τ_cI) / 2
|
|
τ_th = (1 - 2 * τ_c) / 2
|
|
τ_i_on = (τ_thI - 2*τ_i)
|
|
timings_H = (0, τ_c, τ_c + τ_th, 2*τ_c + τ_th)
|
|
|
|
timings_L_hot = (τ_cI, τ_cI + τ_i, τ_cI + τ_i + τ_i_on, τ_cI + 2 * τ_i + τ_i_on)
|
|
|
|
timings_L_cold = tuple(time + timings_H[2] for time in timings_L_hot)
|
|
|
|
return timings_H, (timings_L_cold, timings_L_hot)
|
|
|
|
τ_mod, τ_I = 0.1, 0.1
|
|
(p_H, p_L) = timings(τ_mod, τ_I, .5)
|
|
prototype = OttoEngine(
|
|
δ=[0.4, 0.4],
|
|
ω_c=[2, 2],
|
|
ψ_0=qt.basis([2], [1]),
|
|
description=f"A model for scanning coupling strength and interactin switch times.",
|
|
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=[1, 4],
|
|
therm_methods=["tanhsinh", "tanhsinh"],
|
|
Δ=1,
|
|
num_cycles=4,
|
|
Θ=1.5 / 0.05,
|
|
dt=0.001,
|
|
timings_H=p_H,
|
|
timings_L=p_L,
|
|
streaming_mode=True,
|
|
shift_to_resonance=(False, False),
|
|
L_shift=(0.0, 0.0),
|
|
)
|
|
ot.plot_cycle(prototype)
|
|
|
|
overlaps = np.round(np.linspace(0, 1, 3), 3)
|
|
shifts = np.round(np.linspace(0, τ_mod, 3), 3)
|
|
|
|
models = []
|
|
|
|
import itertools
|
|
|
|
for overlap, shift in itertools.product(overlaps, shifts):
|
|
(p_H, p_L) = timings(τ_mod, τ_I, overlap)
|
|
|
|
model = prototype.copy()
|
|
model.timings_H = p_H
|
|
model.timings_L = p_L
|
|
model.L_shift = (shift, shift)
|
|
models.append(model)
|
|
|
|
|
|
ot.plot_cycles(models)
|