I referred to the following Qiskit's document: state_fidelity.

The inputs of state_fidelity are quantum state vectors or density matrices. For example, $|00\rangle$ or $|0\rangle \langle0|$.

Now, let me assume a circuit is described as follows. enter image description here

The above circuit with no noise provides $\frac{1}{\sqrt{2}}( |00\rangle + |11\rangle )$ .

When I would like to measure the noise of this circuit, I use state_fidelity and compare the measurements and $\frac{1}{\sqrt{2}}( |00\rangle + |11\rangle )$. Here, we have to obtain the state vector such as numpy.array([***]).

However, I received the following error: AerError: 'statevector_simulator does not support noise.' when I set backend = Aer.get_backend('statevector_simulator') to get the state vectors.

When I use qasm_simulator with noise_model (cf. How can noise on a device be simulated using measured noise parameters?), I obtained the following error: 'No statevector for experiment "None". However, I could obtain the counts by result().get_counts.

How can I obtain the state vector with noise_model? Do I have to calculate the state vector from the counts?

I append the code as below.

%matplotlib inline
# Importing standard Qiskit libraries and configuring account
from qiskit import QuantumCircuit, execute, Aer, IBMQ
from qiskit.compiler import transpile, assemble
from qiskit.tools.jupyter import *
from qiskit.visualization import *
# Loading your IBM Q account(s)
provider = IBMQ.load_account()

from qiskit.aqua import run_algorithm
from qiskit.aqua.input import LinearSystemInput
from qiskit.quantum_info import state_fidelity
from qiskit.aqua.algorithms.classical import ExactLSsolver

from qiskit import *
import numpy as np

from qiskit.providers.aer import noise
properties = device.properties()
noise_model = noise.device.basic_device_noise_model(properties)
basis_gates = noise_model.basis_gates

def test():
    q = QuantumRegister(2)
    c = ClassicalRegister(2)
    qc = QuantumCircuit(q,c) 
    qc.h( q[0] )
    qc.cx( q[0], q[1] )


    return qc
# test().draw()

def test2():
    q = QuantumRegister(2)
    c = ClassicalRegister(2)
    qc = QuantumCircuit(q,c) 
    qc.h( q[0] )
    qc.cx( q[0], q[1] )

#     qc.measure(q,c)

    return qc
# test2().draw()
backend = Aer.get_backend('qasm_simulator')
simulator = Aer.get_backend( 'qasm_simulator' )
job1 = execute( test(), backend, noise_model = noise_model, basis_gates= basis_gates, shots = 100 ).result().get_statevector()

backend = Aer.get_backend('statevector_simulator')
simulator = Aer.get_backend( 'statevector_simulator' )
job2 = execute( test2(), backend, noise_model=noise_model, shots = 100 ).result().get_statevector()

Thank you.


1 Answer 1


The reason the Statevector simulator does not handle noise is that Statevector is not a correct formulation, mathematically, for the application of noise; for this one has to work with a generalization of statevector called Density Matrix.

Obtaining the density matrix of a noisy state from the actual measurements is done by something called Quantum State Tomography which is part of qiskit-ignis. See here the tutorial.

There is, however, an alternative method which skips using the simulator altogether. You can use qiskit-terra's quantum_info library to directly obtain the quantum operator for the circuit, and compose it with a quantum operator for the noise. This requires a little more understanding of the underlying math, but the results are precise, as opposed to using state tomography which is intended for "real world" use, where you don't have a noise model.

  • $\begingroup$ I will just add that when theoretically looking at noise, the density matrix approach suggested here is a good one. However, experimentally there is always a state vector to which noise is applied. A density matrix looks at the ensemble average of experiments that possibly include noise. There are other simulation techniques that look at single-shot realizations of noise, but these are not implemented in Qiskit Aer. $\endgroup$ Jan 28, 2020 at 10:52
  • $\begingroup$ Indeed, the "standard" qiskit simulator actually uses statevector as its inner data structure and changes it according to the noise. However, it means that each shot would result in a different statevector, and this output format is indeed not supported in qiskit (which outputs measurement distributions, and provides the statevector only in purely unitary simulation) $\endgroup$
    – Gadi A
    Jan 28, 2020 at 11:50
  • $\begingroup$ Thank you for your comments. I would like to ask one question. Let assume the following result with noise where the number of shots is 100: |00>: 40, |01>:10, |10>:10, and |11>:40. This result approximately provides |s_noise> = 0.63|00> + 0.32|01 + 0.32|10> + 0.63|11> . The result with no noise provides |s_pure> = 0.71|00> + 0.71|11>. Here, what is the meaning of the inner product <s_noise|s_pure> ? What is the difference with that of Density Matrix? $\endgroup$
    – Ashy
    Jan 29, 2020 at 0:54

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