def count_state_averge(val):
val = np.abs(val)
print(val)
values = tools.all_binary_combs(size=3)
s = (8, 8)
init_data = np.zeros(s)
df = pd.DataFrame(data=val, index=values[::-1], columns=values)
sns.heatmap(df, cmap='RdYlGn_r', linewidths=0.5, annot=True).set_title(
"Šredingerio lygties perėjimo matricos reikšmių moduliai")
plt.show()
def test_oracul_values():
test_case = [test_oracle_1, test_oracle_2]
for test in range(0, len(test_case)):
values = tools.all_binary_combs(size=4)
s = (16, 16)
init_data = np.zeros(s)
df = pd.DataFrame(data=init_data, index=values[::-1], columns=values)
# print(df)
for b in values:
qr = QuantumRegister(5, "qr")
cr = ClassicalRegister(5, "cr")
qc = QuantumCircuit(qr, cr)
for i in range(0, len(b)):
if b[i] == '1':
qc.x(qr[3 - i])
apply_test = test_case[test]
apply_test(qr, cr, qc)
# test_oracle_1(qr,cr,qc)
# test_oracle_2(qr, cr, qc)
qc.measure(qr[0], cr[0])
qc.measure(qr[1], cr[1])
qc.measure(qr[2], cr[2])
qc.measure(qr[3], cr[3])
qc.measure(qr[4], cr[4])
# tools.simulate_and_show_result(qc, str(b))
result = tools.simulate_and_return_result(qc)
keys = list(result.keys())
val = list(result.values())
for i in range(0, len(keys)):
value_cubits = keys[i][1:5]
control_cubit = keys[i][0]
if control_cubit == "1":
df[str(b)][value_cubits] = val[i] / 1024
sns.heatmap(df, cmap='RdYlGn_r', linewidths=0.5,
annot=True).set_title("Tikimybė, kad $ q_{n+1} = 1 $"
" pritaikius $U_{f_" +
str(test + 1) + "}$")
plt.xlabel('Kubitų $ q_0, q_1, ..., q_{n}$ įvestis',
fontsize=10) # x-axis label with fontsize 15
plt.ylabel('Galimos $ q_0, q_1, ..., q_{n}$ reikšmės',
fontsize=10) # y-axis label with fontsize 15
plt.show()
def test_all_grover_values_bell():
values = tools.all_binary_combs(size=4)
s = (16, 16)
init_data = np.zeros(s)
df = pd.DataFrame(data=init_data, index=values[::-1], columns=values)
for j in range(0, 16):
result = grover_with_bell_state(j)
keys = list(result.keys())
val = list(result.values())
for i in range(0, len(keys)):
df[values[j]][keys[i]] = val[i] / 1024
sns.heatmap(df, cmap='RdYlGn_r', linewidths=0.5, annot=True).set_title(
"Groverio paieškos tikimybės Bello būsenoje su skirtingais $U_{f}$ ")
plt.xlabel('Orakulo $U_{f}$ reikšmės', fontsize=10) # x-axis label with fontsize 15
plt.ylabel('Galimos būsenos atlikus matavimą', fontsize=10) # y-axis label with fontsize 15
plt.show()