How to import Yao.jl into IBM Qiskit Python code?

[OuCheng-Fu]

I am doing a project involving Variational QITE using IBM Qiskit, the code is as the following:

`from qiskit.circuit.library import EfficientSU2

observable = qubitOp
ansatz = EfficientSU2(observable.num_qubits, reps=3)
ansatz.decompose().draw('mpl')

from qiskit.algorithms import TimeEvolutionProblem, VarQITE
from qiskit.algorithms.time_evolvers.variational import ImaginaryMcLachlanPrinciple
from qiskit.quantum_info import SparsePauliOp
from qiskit.algorithms.gradients import ReverseEstimatorGradient, ReverseQGT

parameters = list(ansatz.parameters)
init_param_values = np.zeros(len(parameters))
for i in range(len(parameters)):
init_param_values[i] = np.pi / 4

var_principle = ImaginaryMcLachlanPrinciple(qgt = ReverseQGT() , gradient = ReverseEstimatorGradient())
evo_gradient = var_principle.evolution_gradient(observable, ansatz, init_param_values, gradient_params = None)
print(evo_gradient)

time = 1
aux_ops = [observable]
evolution_problem = TimeEvolutionProblem(observable, time, aux_operators=aux_ops)
evolution_params = evolution_problem.validate_params()
print(evolution_problem)
print(evolution_params)

from qiskit_aer.estimator import Estimator
var_qite = VarQITE(ansatz, init_param_values, var_principle, Estimator(), ode_solver="RK45", num_timesteps=100, imag_part_tol=1e-07)
evolution_result = var_qite.evolve(evolution_problem)
print(evolution_result)`

If I want to use Yao.jl to speed up its calculation, what should I import? Since the computational runtime of RK45 and Reverse Estimator Gradient is still too long (1700 minutes for my smaller case, more than 60 days for my more complicated case, even without a result)