Exact simulation with Qiskit primitives

The reference primitives in Qiskit can perform local statevector simulations, which is useful for quickly prototyping algorithms.

The Estimator primitive can compute an expectation value, and the Sampler primitive can compute circuit output probabilities.

Compute an expectation value with the Estimator primitive

Follow these instructions to get the expected value of an observable for a given quantum circuit with the qiskit.primitives.Estimator primitive.

from qiskit.primitives import BackendEstimator
from <some_qiskit_provider> import QiskitProvider
 
provider = QiskitProvider()
backend = provider.get_backend('backend_name')
estimator = BackendEstimator(backend)

Initialize observables

The first step is to define the observables whose expected value you want to compute. Each observable can be any BaseOperator, like the operators from qiskit.quantum_info. Among them it is preferable to use qiskit.quantum_info.SparsePauliOp.

from qiskit.quantum_info import SparsePauliOp
 
observable = SparsePauliOp(["II", "XX", "YY", "ZZ"], coeffs=[1, 1, -1, 1])

Initialize QuantumCircuit

Next, create the qiskit.circuit.QuantumCircuits for which you want to obtain the expected value.

from qiskit import QuantumCircuit
 
qc = QuantumCircuit(2)
qc.h(0)
qc.cx(0,1)
qc.draw("mpl")

Initialize Estimator

Next, instantiate an qiskit.primitives.Estimator.

from qiskit.primitives import Estimator
 
estimator = Estimator()

Run and get results

Now that you have defined your estimator, you can run your estimation by calling the qiskit.primitives.Estimator.run method, which returns an instance of qiskit.providers.JobV1. You can get the results from the job (as a qiskit.primitives.EstimatorResult object) with the qiskit.providers.JobV1.result method.

job = estimator.run(qc, observable)
result = job.result()
print(result)

EstimatorResult(values=array([4.]), metadata=[{}])

This example only uses one qiskit.circuit.QuantumCircuit and one observable. If you want to get expectation values for multiple circuits and observables, you can pass a list of qiskit.circuit.QuantumCircuits and a list of BaseOperators to the qiskit.primitives.Estimator.run method. Both lists must have the same length.

Get the expected value

From these results you can extract the expected values with the attribute qiskit.primitives.EstimatorResult.values.

qiskit.primitives.EstimatorResult.values returns a numpy.ndarray whose i^th element is the expectation value corresponding to the i^th circuit and i^th observable.

exp_value = result.values[0]
print(exp_value)

3.999999999999999

Parameterized circuit with Estimator

The qiskit.primitives.Estimator primitive can be run with unbound parameterized circuits like the one below. You can also manually bind values to the parameters of the circuit and follow the steps of the previous example.

from qiskit.circuit import Parameter
 
theta = Parameter('θ')
param_qc = QuantumCircuit(2)
param_qc.ry(theta, 0)
param_qc.cx(0,1)
print(param_qc.draw())

     ┌───────┐
q_0: ┤ Ry(θ) ├──■──
     └───────┘┌─┴─┐
q_1: ─────────┤ X ├
              └───┘

The main difference with the previous case is that now you need to specify the sets of parameter values for which you want to evaluate the expectation value as a list of lists of floats. The i^th element of the outer list is the set of parameter values that corresponds to the i^th circuit and observable.

import numpy as np
 
parameter_values = [[0], [np.pi/6], [np.pi/2]]
 
job = estimator.run([param_qc]*3, [observable]*3, parameter_values=parameter_values)
values = job.result().values
 
for i in range(3):
    print(f"Parameter: {parameter_values[i][0]:.5f}\t Expectation value: {values[i]}")

Parameter: 0.00000   Expectation value: 2.0
Parameter: 0.52360   Expectation value: 3.0
Parameter: 1.57080   Expectation value: 4.0

Change run options

Your workflow might require tuning primitive run options, such as the number of shots.

By default, the reference qiskit.primitives.Estimator class performs an exact statevector calculation based on the qiskit.quantum_info.Statevector class. However, this can be modified to include shot noise if the number of shots is set. For reproducibility purposes, a seed will also be set in the following examples.

There are two main ways of setting options in the qiskit.primitives.Estimator:

• Set keyword arguments in the qiskit.primitives.Estimator.run method.
• Modify qiskit.primitives.Estimator options.

Set keyword arguments for qiskit.primitives.Estimator.run

If you only want to change the settings for a specific run, it can be more convenient to set the options inside the qiskit.primitives.Estimator.run method. You can do this by passing them as keyword arguments.

job = estimator.run(qc, observable, shots=2048, seed=123)
result = job.result()
print(result)

EstimatorResult(values=array([4.]), metadata=[{'variance': 3.552713678800501e-15, 'shots': 2048}])

print(result.values[0])

3.999999998697238

Modify qiskit.primitives.Estimator options

If you want to keep some configuration values for several runs, it can be better to change the qiskit.primitives.Estimator options. That way you can use the same qiskit.primitives.Estimator object as many times as you wish without having to rewrite the configuration values every time you use qiskit.primitives.Estimator.run.

Modify existing qiskit.primitives.Estimator

If you prefer to change the options of an already-defined qiskit.primitives.Estimator, you can use the method qiskit.primitives.Estimator.set_options and introduce the new options as keyword arguments.

estimator.set_options(shots=2048, seed=123)
 
job = estimator.run(qc, observable)
result = job.result()
print(result)

EstimatorResult(values=array([4.]), metadata=[{'variance': 3.552713678800501e-15, 'shots': 2048}])

print(result.values[0])

3.999999998697238

Define a new qiskit.primitives.Estimator with the options

If you prefer to define a new qiskit.primitives.Estimator with new options, define a dict like this one:

options = {"shots": 2048, "seed": 123}

You can then introduce it into your new qiskit.primitives.Estimator with the options argument.

estimator = Estimator(options=options)
 
job = estimator.run(qc, observable)
result = job.result()
print(result)

EstimatorResult(values=array([4.]), metadata=[{'variance': 3.552713678800501e-15, 'shots': 2048}])

print(result.values[0])

3.999999998697238

Compute circuit output probabilities with Sampler primitive

Follow these instructions to get the probability distribution of a quantum circuit with the qiskit.primitives.Sampler primitive.

from qiskit.primitives import BackendSampler
from <some_qiskit_provider> import QiskitProvider
 
provider = QiskitProvider()
backend = provider.get_backend('backend_name')
sampler = BackendSampler(backend)

Initialize QuantumCircuit

The first step is to create the qiskit.circuit.QuantumCircuits from which you want to obtain the probability distribution.

from qiskit import QuantumCircuit
 
qc = QuantumCircuit(2)
qc.h(0)
qc.cx(0,1)
qc.measure_all()
qc.draw("mpl")

Initialize Sampler

Next, create a qiskit.primitives.Sampler instance.

from qiskit.primitives import Sampler
 
sampler = Sampler()

Run and get results

Now that you have defined your sampler, run it by calling the qiskit.primitives.Sampler.run method, which returns an instance of qiskit.providers.JobV1. You can get the results from the job (as a qiskit.primitives.SamplerResult object) with the qiskit.providers.JobV1.result method.

job = sampler.run(qc)
result = job.result()
print(result)

SamplerResult(quasi_dists=[{0: 0.4999999999999999, 3: 0.4999999999999999}], metadata=[{}])

While this example only uses one qiskit.circuit.QuantumCircuit, you can sample multiple circuits by passing a list of qiskit.circuit.QuantumCircuit instances to the qiskit.primitives.Sampler.run method.

Get the probability distribution

From these results you can extract the quasi-probability distributions with the attribute qiskit.primitives.SamplerResult.quasi_dists.

Even though there is only one circuit in this example, qiskit.primitives.SamplerResult.quasi_dists returns a list of qiskit.result.QuasiDistributions. result.quasi_dists[i] is the quasi-probability distribution of the i^th circuit.

quasi_dist = result.quasi_dists[0]
print(quasi_dist)

{0: 0.4999999999999999, 3: 0.4999999999999999}

Probability distribution with binary outputs

If you prefer to see the output keys as binary strings instead of decimal numbers, you can use the qiskit.result.QuasiDistribution.binary_probabilities method.

print(quasi_dist.binary_probabilities())

{'00': 0.4999999999999999, '11': 0.4999999999999999}

Parameterized circuit with Sampler

The qiskit.primitives.Sampler primitive can be run with unbound parameterized circuits like the one below. You can also manually bind values to the parameters of the circuit and follow the steps of the previous example.

from qiskit.circuit import Parameter
 
theta = Parameter('θ')
param_qc = QuantumCircuit(2)
param_qc.ry(theta, 0)
param_qc.cx(0,1)
param_qc.measure_all()
print(param_qc.draw())

          ┌───────┐      ░ ┌─┐   
     q_0: ┤ Ry(θ) ├──■───░─┤M├───
          └───────┘┌─┴─┐ ░ └╥┘┌─┐
     q_1: ─────────┤ X ├─░──╫─┤M├
                   └───┘ ░  ║ └╥┘
  meas: 2/══════════════════╩══╩═
                            0  1 

The main difference from the previous case is that now you need to specify the sets of parameter values for which you want to evaluate the expectation value as a list of lists of floats. The i^th element of the outer list is the set of parameter values that corresponds to the i^th circuit.

import numpy as np
 
parameter_values = [[0], [np.pi/6], [np.pi/2]]
 
job = sampler.run([param_qc]*3, parameter_values=parameter_values)
dists = job.result().quasi_dists
 
for i in range(3):
    print(f"Parameter: {parameter_values[i][0]:.5f}\t Probabilities: {dists[i]}")

Parameter: 0.00000   Probabilities: {0: 1.0}
Parameter: 0.52360   Probabilities: {0: 0.9330127018922194, 3: 0.0669872981077807}
Parameter: 1.57080   Probabilities: {0: 0.5000000000000001, 3: 0.4999999999999999}

Change run options

Your workflow might require tuning primitive run options, such as the number of shots.

By default, the reference qiskit.primitives.Sampler class performs an exact statevector calculation based on the qiskit.quantum_info.Statevector class. However, this can be modified to include shot noise if the number of shots is set. For reproducibility purposes, a seed will also be set in the following examples.

There are two main ways of setting options in the qiskit.primitives.Sampler:

• Set keyword arguments in the qiskit.primitives.Sampler.run method.
• Modify qiskit.primitives.Sampler options.

Set keyword arguments for qiskit.primitives.Sampler.run

If you only want to change the settings for a specific run, it can be more convenient to set the options inside the qiskit.primitives.Sampler.run method. You can do this by passing them as keyword arguments.

job = sampler.run(qc, shots=2048, seed=123)
result = job.result()
print(result)

SamplerResult(quasi_dists=[{0: 0.5205078125, 3: 0.4794921875}], metadata=[{'shots': 2048}])

Modify qiskit.primitives.Sampler options

If you want to keep some configuration values for several runs, it can be better to change the qiskit.primitives.Sampler options. That way you can use the same qiskit.primitives.Sampler object as many times as you wish without having to rewrite the configuration values every time you use qiskit.primitives.Sampler.run.

Modify existing qiskit.primitives.Sampler

If you prefer to change the options of an already-defined qiskit.primitives.Sampler, you can use qiskit.primitives.Sampler.set_options and introduce the new options as keyword arguments.

sampler.set_options(shots=2048, seed=123)
 
job = sampler.run(qc)
result = job.result()
print(result)

SamplerResult(quasi_dists=[{0: 0.5205078125, 3: 0.4794921875}], metadata=[{'shots': 2048}])

Define a new qiskit.primitives.Sampler with the options

If you prefer to define a new qiskit.primitives.Sampler with new options, define a dict like this one:

options = {"shots": 2048, "seed": 123}

You can then introduce it into your new qiskit.primitives.Sampler with the options argument.

sampler = Sampler(options=options)
 
job = sampler.run(qc)
result = job.result()
print(result)

SamplerResult(quasi_dists=[{0: 0.5205078125, 3: 0.4794921875}], metadata=[{'shots': 2048}])