plot_bloch.py

import qutip
import matplotlib.pyplot as plt
import numpy as np

b = qutip.Bloch()
# vec = [0,1,0]
th = np.linspace(0, np.pi, 20)
xz = np.sin(th)
yz = np.zeros(20)
zz = np.cos(th)
# b.add_vectors(vec)
b.clear()
# b.add_points([xz,yz,zz])
b.add_vectors(list(zip(xz,yz,zz)))
b.vector_color=["r"]
# b.add_vectors([xz,yz,zz])
b.make_sphere()
plt.show()

qubit_y_rotation.py

from math import pi
import matplotlib.pyplot as plt
from qiskit import *
from qiskit.tools.visualization import plot_bloch_multivector, plot_histogram
from qiskit.quantum_info import state_fidelity
from qiskit import BasicAer
import numpy as np
n = 7
qc = QuantumCircuit(n)

th_res = []  # theoretical results
for i in range(n):
    θ = pi*i/(n-1)
    qc.u(θ, 0, 0, i)
    # compute theoretical results
    tmp = {"0": np.cos(θ/2)**2, "1": np.sin(θ/2)**2}
    th_res.append(tmp)

qc.barrier()
qc.measure_all()

qc.draw()

backend = BasicAer.get_backend('qasm_simulator')
# uncomment next 2 lines to use a quantum computer from IBM
# provider = IBMQ.get_provider("ibm-q")
# backend = provider.get_backend("ibm_perth") 
shots = 1024
results = execute(qc, backend=backend, shots=shots).result()
answer = results.get_counts()

res = [{"0": 0, "1": 0} for _ in range(n)]
for i in range(n):
    for k, v in answer.items():
        res[i][k[i]] += v
    res[i]["0"] /= shots
    res[i]["1"] /= shots

df_th = pd.DataFrame(th_res)
df_res = pd.DataFrame(res[::-1]) # reversed order