qc2 = QuantumCircuit(q2)
qc2.x(q2)
qc2.h(q2[0])
qc2.cx(q2[0], q2[1])
# In[5]:
#Dibujo del circuito
qc.draw(output='mpl')
# In[6]:
#State vector
backend = Aer.get_backend('statevector_simulator')
Statevector1 = execute(qc, backend).result().get_statevector()
array_to_latex(Statevector1, pretext="\\text{Statevector1 = }")
# In[7]:
#Dibujo del segundo circuito
qc2.draw(output='mpl')
# In[8]:
#State vector segundo circuito
backend = Aer.get_backend('statevector_simulator')
Statevector2 = execute(qc2, backend).result().get_statevector()
array_to_latex(Statevector2, pretext="\\text{Statevector2 = }")
# In[9]:
n = 2
t = 2
N = 1
M = 4
qc = QuantumCircuit(n,t)
GC = initialize_s(qc, range(n))
GC.measure_all()
#ceate the CR1 schedule
# The Unitary is an identity (with a global phase)
backend = qiskit.Aer.get_backend('unitary_simulator')
basis_gates = ['u1','u2','u3','cx'] # use U,CX for now
job = qiskit.execute(qc, backend=backend, basis_gates=basis_gates)
from qiskit_textbook.tools import array_to_latex
array_to_latex(np.around(job.result().get_unitary(),3), pretext="\\text{Unitary} = ")
#Add the CR1 instruction to basis_gates and inst_map
basis_gates += ['cr1']
inst_map.add(gate_name, [1,0], sched)
#Create a quantum gate to reference the CR1 pulse schedule
cr1_gate = Gate(gate_name, 2, [])
#Create the QPT circuits
qregs = QuantumRegister(config.n_qubits)
circuit = QuantumCircuit(qregs)
circuit.append(cr1_gate, qargs = [qregs[1], qregs[0]])
qpt_circuits = process_tomography_circuits(circuit, [qregs[0], qregs[1]])
#Create the QPT pulse scchedules
# In[2]:
#Construimos un circuito con 3 registros
qc = QuantumCircuit(3)
#aplicamos la compuerta H al primer registro
qc.h(0)
#luego aplicamos CNOT en registros 0 y 1, luego 1 y 2
qc.cx(0, 1)
qc.cx(1, 2)
# In[3]:
#Dibujo del circuito
qc.draw(output='mpl')
# In[4]:
#State vector
backend = Aer.get_backend('statevector_simulator')
final_state = execute(qc, backend).result().get_statevector()
array_to_latex(final_state, pretext="\\text{Statevector = }")
# In[5]:
#Histograma de los resultados
results = execute(qc, backend).result().get_counts()
plot_histogram(results)
# In[6]:
qc.h(q[0])
# In[30]:
#Dibujo del circuito
qc.draw(output='mpl')
# In[31]:
#Mostramos resultado como compuerta unitaria, se puede observar como resultado el CNOT
backend = Aer.get_backend('unitary_simulator')
unitary = execute(qc, backend).result().get_unitary()
# In[32]:
array_to_latex(unitary, pretext="\\text{Circuit = }\n")
# In[33]:
#Resultado del state vector
backend = Aer.get_backend('statevector_simulator')
final_state = execute(qc, backend).result().get_statevector()
array_to_latex(final_state, pretext="\\text{Statevector = }")
# In[34]:
results = execute(qc, backend).result().get_counts()
plot_histogram(results)
# In[ ]:
import numpy as np
import matplotlib.pyplot as plt
from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister, execute, BasicAer, IBMQ
from qiskit.extensions import Initialize
from qiskit.visualization import plot_histogram, plot_bloch_multivector
from qiskit_textbook.tools import random_state, array_to_latex
psi = random_state(1)
# Display it nicely
array_to_latex(psi, pretext="|\\psi\\rangle =")
# Show it on a Bloch sphere
plot_bloch_multivector(psi)
plt.show()
init_gate = Initialize(psi)
init_gate.label = "init"
inverse_init_gate = init_gate.gates_to_uncompute()
def create_bell_pair(qc, a, b):
qc.h(a)
qc.cx(a, b)
def alice_gates(qc, psi, a):
qc.cx(psi, a)
qc.h(psi)