I have a simple parameterized quantum circuit which looks like the image attached. Here is the code used to create it:

from qiskit import QuantumCircuit, Aer, execute
import numpy as np
from qiskit.circuit import ParameterVector, QuantumCircuit

def add_layer(kernel, params, n_qubits):

    qubits = list(range(n_qubits))

    for i in range(n_qubits):
        kernel.rx(params[i], qubits[i])

    for i in range(n_qubits):
        kernel.ry(params[i + n_qubits], qubits[i])

    for i in range(n_qubits):
        kernel.rz(params[i + n_qubits*2], qubits[i])

    for q1, q2 in zip(qubits[0::2], qubits[1::2]):
        kernel.cz(qubits[q1], qubits[q2])

    return kernel

n_qubits = 2
n_samples = 5
simulator = Aer.get_backend('statevector_simulator')

n_parameters = n_qubits*3
parameters = np.random.default_rng(13).uniform(low=0, high=1, size = (n_samples,n_parameters))

p = ParameterVector('p', length = n_parameters)  

kernel = QuantumCircuit(n_qubits)
kernel = add_layer(kernel, p, n_qubits)

bc = kernel.bind_parameters({p: parameters[0]})

result = execute(bc, simulator).result().get_statevector()

Currently I am only running this for parameters[0] but I would like to run this for all 5 samples in parameters. How do I bind all of them?

Moreover, I would like to calculate the Z expectation value of the final qubit from the statevector with a built in method if there is one available.

Thanks for your help.


1 Answer 1


The method bind_parameters() assigns the parameters to a copy of the circuit and returns it, while keeping the current circuit untouched. So, you can call bind_parameters() multiple times on the same circuit and pass a different set of values each time.

To calculate the expectation value of an operator against a Statevector, you can use Statevector.expectation_value() method

from qiskit.quantum_info.operators.symplectic import Pauli

op = Pauli('ZI')

for m in range(n_samples):
    bc = kernel.bind_parameters({p: parameters[m]})
    result = execute(bc, simulator).result().get_statevector()

    expectation_value = result.expectation_value(op)
  • $\begingroup$ Doing this in a for loop is costly. I am thinking of a QNN workflow where I would like to pass x_train to my parameterized ansatz. How does one deal with that workflow? $\endgroup$ Apr 20, 2023 at 11:44

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