pdc-quantum

import numpy as np
from qiskit import QuantumCircuit

# define the state vector
init_list = np.array( [0., 0., 0., 0., 0., 0., 0., 6.**.5,
                       0., +1., +1., 0., +1., 0., 0., 0.,
                       0., 0., 0., +1., 0., +1., +1., 0.,
                       6.**.5, 0., 0., 0., 0., 0., 0., 0.]) / np.sqrt(18)

# create the circuit

pdc-quantum

from qiskit import execute
from qiskit.providers.aer import AerSimulator
from qiskit.test.mock import FakeSantiago
from qiskit.tools.visualization import plot_histogram

device_backend = FakeSantiago()
sim_backend= AerSimulator.from_backend(device_backend)
job = execute(circuit, backend = sim_backend, shots = 8192,
              optimization_level = 2)
counts_noise = job.result().get_counts()

pdc-quantum

import numpy as np
from qiskit import QuantumCircuit

init_list = np.array([0., 0., 0., 2., 0., -1., -1., 0.,
             0., -1., -1., 0., 2., 0., 0., 0.]) / ( 2*np.sqrt(3) )

circuit = QuantumCircuit(4)
circuit.initialize(init_list, circuit.qubits)

# add Pauli-Z measurements and draw the circuit

pdc-quantum

import numpy as np

# settings:
qubits = [0,1]
lengths = np.arange(1, 260, 28)
num_samples = 20
seed = None

# additional information:
system = 'ibmq_manila'

pdc-quantum

import matplotlib.pyplot as plt

with h_model:    
    az.plot_posterior(trace_h_model, var_names = ["EPC"],
                      round_to = 4, figsize = [10,6], textsize = 12)                          
    Bayes_legend =  "EPC  Bayesian:    {0:1.3e} ± {1:1.3e}"\
                      .format(az_summary['mean']['EPC'], az_summary['sd']['EPC'])
    LSF_legend =    "EPC  Frequentist: {0:1.3e} ± {1:1.3e}"\
                      .format(epc_est, epc_est_err)      
    plt.axvline(x=az_summary['mean']['EPC'], color='blue', ls="--")

pdc-quantum

with h_model:
    az_summary = az.summary(trace_h_model, round_to = 12,                            
                             var_names = ["π", "EPC"],
                             kind = "stats")
az_summary

pdc-quantum

with h_model:
    az.plot_posterior(trace_h_model, var_names = ["θ"],
                      round_to = 4, figsize = [16, 12]);

pdc-quantum

with h_model:
    az.plot_posterior(trace_h_model, var_names = ["σ_Beta"],
                      round_to = 4, figsize = [16, 12]);

pdc-quantum

with h_model:
    az.plot_posterior(trace_h_model, var_names = ["π"],
                      round_to = 4, figsize = [15, 3]);

pdc-quantum

with h_model:
    trace_h_model = pm.sample(draws = 2000, tune = 500, target_accept = .95,
                         return_inferencedata = True, chains = 4)
    az.plot_trace(trace_h_model);