Primitives examples
The examples in this section illustrate some common ways to use primitives. Before running these examples, follow the instructions in Install and set up.
Note
These examples all use the primitives from Qiskit Runtime, but you could use the base primitives instead.
Estimator examples
Efficiently calculate and interpret expectation values of the quantum operators required for many algorithms with Estimator. Explore uses in molecular modeling, machine learning, and complex optimization problems.
Run a single experiment
Use Estimator to determine the expectation value of a single circuit-observable pair.
import numpy as np
from qiskit.circuit.library import IQP
from qiskit.quantum_info import SparsePauliOp, random_hermitian
from qiskit_ibm_runtime import QiskitRuntimeService, Estimator
service = QiskitRuntimeService()
backend = service.get_backend("ibm_brisbane")
n_qubits = 127
mat = np.real(random_hermitian(n_qubits, seed=1234))
circuit = IQP(mat)
observable = SparsePauliOp("Z" * n_qubits)
estimator = Estimator(backend)
job = estimator.run(circuit, observable)
result = job.result()
print(f" > Observable: {observable.paulis}")
print(f" > Expectation value: {result.values}")
print(f" > Metadata: {result.metadata}")Run multiple experiments in a single job
Use Estimator to determine the expectation values of multiple circuit-observable pairs.
import numpy as np
from qiskit.circuit.library import IQP
from qiskit.quantum_info import SparsePauliOp, random_hermitian
from qiskit_ibm_runtime import QiskitRuntimeService, Estimator
service = QiskitRuntimeService()
backend = service.get_backend("ibm_brisbane")
n_qubits = 127
rng = np.random.default_rng()
mats = [np.real(random_hermitian(n_qubits, seed=rng)) for _ in range(3)]
circuits = [IQP(mat) for mat in mats]
observables = [
SparsePauliOp("X" * n_qubits),
SparsePauliOp("Y" * n_qubits),
SparsePauliOp("Z" * n_qubits),
]
estimator = Estimator(backend)
job = estimator.run(circuits, observables)
result = job.result()
print(f" > Expectation values: {result.values}")Run parameterized circuits
Use Estimator to run three experiments in a single job, leveraging parameter values to increase circuit reusability.
import numpy as np
from qiskit.circuit.library import RealAmplitudes
from qiskit.quantum_info import SparsePauliOp
from qiskit_ibm_runtime import QiskitRuntimeService, Estimator
service = QiskitRuntimeService()
backend = service.get_backend("ibm_brisbane")
circuit = RealAmplitudes(num_qubits=127, reps=2)
# Define three sets of parameters for the circuit
rng = np.random.default_rng(1234)
parameter_values = [
rng.uniform(-np.pi, np.pi, size=circuit.num_parameters) for _ in range(3)
]
observable = SparsePauliOp("Z" * 127)
estimator = Estimator(backend)
job = estimator.run([circuit] * 3, [observable] * 3, parameter_values)
result = job.result()
print(f" > Expectation values: {result.values}")Use sessions and advanced options
Explore sessions and advanced options to optimize circuit performance on quantum systems.
import numpy as np
from qiskit.circuit.library import IQP
from qiskit.quantum_info import SparsePauliOp, random_hermitian
from qiskit_ibm_runtime import QiskitRuntimeService, Session, Estimator, Options
n_qubits = 127
rng = np.random.default_rng(1234)
mat = np.real(random_hermitian(n_qubits, seed=rng))
circuit = IQP(mat)
mat = np.real(random_hermitian(n_qubits, seed=rng))
another_circuit = IQP(mat)
observable = SparsePauliOp("X" * n_qubits)
another_observable = SparsePauliOp("Y" * n_qubits)
options = Options()
options.optimization_level = 2
options.resilience_level = 2
service = QiskitRuntimeService()
backend = service.get_backend("ibm_brisbane")
with Session(service=service, backend=backend) as session:
estimator = Estimator(session=session, options=options)
job = estimator.run(circuit, observable)
another_job = estimator.run(another_circuit, another_observable)
result = job.result()
another_result = another_job.result()
# first job
print(f" > Expectation values job 1: {result.values}")
# second job
print(f" > Expectation values job 2: {another_result.values}")Sampler examples
Generate entire error-mitigated quasi-probability distributions sampled from quantum circuit outputs. Leverage Sampler’s capabilities for search and classification algorithms like Grover’s and QVSM.
Run a single experiment
Use Sampler to determine the quasi-probability distribution of a single circuit.
import numpy as np
from qiskit.circuit.library import IQP
from qiskit.quantum_info import random_hermitian
from qiskit_ibm_runtime import QiskitRuntimeService, Sampler
service = QiskitRuntimeService()
backend = service.get_backend("ibm_brisbane")
n_qubits = 127
mat = np.real(random_hermitian(n_qubits, seed=1234))
circuit = IQP(mat)
circuit.measure_all()
sampler = Sampler(backend)
job = sampler.run(circuit)
result = job.result()
print(f" > Quasi-probability distribution: {result.quasi_dists}")
print(f" > Metadata: {result.metadata}")Run multiple experiments in a single job
Use Sampler to determine the quasi-probability distributions of multiple circuits in one job.
import numpy as np
from qiskit.circuit.library import IQP
from qiskit.quantum_info import random_hermitian
from qiskit_ibm_runtime import QiskitRuntimeService, Sampler
service = QiskitRuntimeService()
backend = service.get_backend("ibm_brisbane")
n_qubits = 127
rng = np.random.default_rng()
mats = [np.real(random_hermitian(n_qubits, seed=rng)) for _ in range(3)]
circuits = [IQP(mat) for mat in mats]
for circuit in circuits:
circuit.measure_all()
sampler = Sampler(backend)
job = sampler.run(circuits)
result = job.result()
print(f" > Quasi-probability distribution: {result.quasi_dists}")Run parameterized circuits
Run three experiments in a single job, leveraging parameter values to increase circuit reusability.
import numpy as np
from qiskit.circuit.library import RealAmplitudes
from qiskit_ibm_runtime import QiskitRuntimeService, Sampler
service = QiskitRuntimeService()
backend = service.get_backend("ibm_brisbane")
circuit = RealAmplitudes(num_qubits=127, reps=2)
circuit.measure_all()
# Define three sets of parameters for the circuit
rng = np.random.default_rng(1234)
parameter_values = [
rng.uniform(-np.pi, np.pi, size=circuit.num_parameters) for _ in range(3)
]
sampler = Sampler(backend)
job = sampler.run([circuit] * 3, parameter_values)
result = job.result()
print(f" > Quasi-probability distribution: {result.quasi_dists}")Use sessions and advanced options
Explore sessions and advanced options to optimize circuit performance on quantum systems.
import numpy as np
from qiskit.circuit.library import IQP
from qiskit.quantum_info import random_hermitian
from qiskit_ibm_runtime import QiskitRuntimeService, Sampler, Session, Options
n_qubits = 127
rng = np.random.default_rng(1234)
mat = np.real(random_hermitian(n_qubits, seed=rng))
circuit = IQP(mat)
circuit.measure_all()
mat = np.real(random_hermitian(n_qubits, seed=rng))
another_circuit = IQP(mat)
another_circuit.measure_all()
options = Options()
options.optimization_level = 2
options.resilience_level = 0
service = QiskitRuntimeService()
backend = service.get_backend("ibm_brisbane")
with Session(service=service, backend=backend) as session:
sampler = Sampler(session=session, options=options)
job = sampler.run(circuit)
another_job = sampler.run(another_circuit)
result = job.result()
another_result = another_job.result()
# first job
print(f" > Quasi-probability distribution job 1: {result.quasi_dists}")
# second job
print(f" > Quasi-probability distribution job 2: {another_result.quasi_dists}")Next steps
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