Ejemplos de Sampler

Versiones de paquetes

El código de esta página fue desarrollado con los siguientes requisitos. Recomendamos usar estas versiones o más recientes.

qiskit[all]~=2.5.2
qiskit-ibm-runtime~=0.47.0

Genera distribuciones completas de cuasiprobabilidad con mitigación de errores, muestreadas a partir de las salidas de los circuits cuánticos. Aprovecha las capacidades de Sampler en algoritmos de búsqueda y clasificación como el de Grover o QVSM.

Ejecutar un solo experimento

Usa Sampler para devolver el resultado de la medición como cadenas de bits o conteos de un solo circuit.

# Added by doQumentation — installs the packages this notebook needs if they are missing
import importlib.util

_needed = {"numpy": "numpy", "qiskit": "qiskit", "qiskit_ibm_runtime": "qiskit-ibm-runtime"}
_missing = [pip for module, pip in _needed.items()
            if importlib.util.find_spec(module) is None]
# One at a time, so a package that fails to install does not block the others
for _pip in _missing:
    %pip install -q {_pip}
if not _missing:
    print("\u2713 All required packages are installed")

import numpy as np
from qiskit.circuit.library import iqp
from qiskit.transpiler import generate_preset_pass_manager
from qiskit.quantum_info import random_hermitian
from qiskit_ibm_runtime import QiskitRuntimeService

# Legacy server-side primitive before qiskit-ibm-runtime 0.50.0
# from qiskit_ibm_runtime import Sampler

# Client-side primitive introduced in qiskit-ibm-runtime 0.50.0
from qiskit_ibm_runtime.executor_sampler import Sampler

n_qubits = 127

service = QiskitRuntimeService()
backend = service.least_busy(
    operational=True, simulator=False, min_num_qubits=n_qubits
)

mat = np.real(random_hermitian(n_qubits, seed=1234))
circuit = iqp(mat)
circuit.measure_all()

pm = generate_preset_pass_manager(backend=backend, optimization_level=1)
isa_circuit = pm.run(circuit)

sampler = Sampler(backend)
job = sampler.run([isa_circuit])
result = job.result()

# Get results for the first (and only) PUB
pub_result = result[0]

print(f" > First ten results: {pub_result.data.meas.get_bitstrings()[:10]}")

> First ten results: ['0111010010000101000101110000000101010000100111110000110011101100001100101111100100010110000110101001100010100001111010000110000', '0010001000101011111011110111001100101010110111001101100111100000011010010000100000011101010011101010000100101001101100000110001', '0010100110101011001001010111010110111011000101110001001011111010011010001000010010010001110010000001100000100110010000110010001', '1111000001010000010111000010100111001110101000100101000001110110001110010100010100010110001000001000100001101100100001101010100', '1100111010001011001011001010111100011100110010110011110010100001011101100110111000010000011110101010110101100001011000000000000', '0010111110001001110000001110001110010001111110100111100001001011000010000111000010011000100100000001001100001110000001100000110', '0110010101111101101111001101011100111000101110101101010100101010010000010000011000000100101110001010010110101001110001010000110', '0000001111111000001101101010111011010001111101101001111110100101100001110010000111011000000010101000100000000001101110000000001', '1011001100100101111000001000100100001011001001100001001010111011001000001010100111010001001110010101110000100001000100101111001', '1111100010100111000011010101000110011011110111011000000010101000100011000001000100000101000110001000000001001011101101110011000']

Ejecutar múltiples experimentos en un solo trabajo

Usa Sampler para devolver el resultado de la medición como cadenas de bits o conteos de múltiples circuits en un trabajo.

import numpy as np
from qiskit.circuit.library import iqp
from qiskit.transpiler import generate_preset_pass_manager
from qiskit.quantum_info import random_hermitian
from qiskit_ibm_runtime import QiskitRuntimeService

# Legacy server-side primitive before qiskit-ibm-runtime 0.50.0
# from qiskit_ibm_runtime import Sampler

# Client-side primitive introduced in qiskit-ibm-runtime 0.50.0
from qiskit_ibm_runtime.executor_sampler import Sampler

n_qubits = 127

service = QiskitRuntimeService()
backend = service.least_busy(
    operational=True, simulator=False, min_num_qubits=n_qubits
)

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()

pm = generate_preset_pass_manager(backend=backend, optimization_level=1)
isa_circuits = pm.run(circuits)

sampler = Sampler(mode=backend)
job = sampler.run(isa_circuits)
result = job.result()

for idx, pub_result in enumerate(result):
    print(
        f" > First five results for pub {idx}: "
        f"{pub_result.data.meas.get_bitstrings()[:5]}"
    )

> First five results for pub 0: ['0000111110010100100011101001111111000010000000100001011110000101000111100110000000000110010000001001000001100000101101000000011', '1000001101000100111010110001100100100011111100001000011010100001001011001110000100100011100010010101000100001110001110000101000', '1010001100001101011000000110100001101000010101000000110010100001100010010000001000011100100101011001000001110101100000100010001', '0010010011001100001001111111001100001011010010010111011010111000010010100011011101101100110000101001001101000000001010000001000', '0000101100100011000111100010110010111000000101010101010101011010010010011011000110011010011001011110010101000111000111000100000']
 > First five results for pub 1: ['1011011001010100010010111001111100011000110010000110000000100101101111100000010000011000100011101000101101001100000000011011000', '1110000000100001100111000010110011101110110010001011100001000000000100110011010010100010000100001111001101001000001000000011000', '0100000010010100010011000011111011010011010111110111110011101100110011111010111101011000110000001111110001110111001110000000001', '1110011011010111110110111000010111101010000110001101001000100000001010001001010000001000001110000000000110001101001010000010000', '1110010111110110100100100010100110101000001000100011100101001001110011001001011101000000000000110101010000100111010100010011000']
 > First five results for pub 2: ['0011110001000000010000101101010100011011101101001111001000011011101010011010011000010001000001010101100100010000011100010000000', '1010110011100101001111110000110111110011101101100011000100001111001001101011000010100001000000100011110001101001001000101100001', '1111000011001100000101010110100010110001000000000111100111100011011101101001100110001010010000000101001000011100100001000011100', '0100101011111000010100001001001110001100001001100111011100010010000001000010100101101000001001101000110011010000001010101000000', '1101011000010000010011001111111011110111011101000011010001000000100010000110001010000101110000111101000110100000100011110110000']

Ejecutar circuits parametrizados

Ejecuta varios experimentos en un solo trabajo, aprovechando los valores de parámetros para aumentar la reutilización del circuit.

import numpy as np
from qiskit.circuit.library import real_amplitudes
from qiskit.transpiler import generate_preset_pass_manager
from qiskit_ibm_runtime import QiskitRuntimeService

# Legacy server-side primitive before qiskit-ibm-runtime 0.50.0
# from qiskit_ibm_runtime import Sampler

# Client-side primitive introduced in qiskit-ibm-runtime 0.50.0
from qiskit_ibm_runtime.executor_sampler import Sampler

n_qubits = 127

service = QiskitRuntimeService()
backend = service.least_busy(
    operational=True, simulator=False, min_num_qubits=n_qubits
)

# Step 1: Map classical inputs to a quantum problem
circuit = real_amplitudes(num_qubits=n_qubits, 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)
]

# Step 2: Optimize problem for quantum execution.

pm = generate_preset_pass_manager(backend=backend, optimization_level=1)
isa_circuit = pm.run(circuit)

# Step 3: Execute using IBM Quantum primitives.
sampler = Sampler(backend)
job = sampler.run([(isa_circuit, parameter_values)])
result = job.result()
# Get results for the first (and only) PUB
pub_result = result[0]
# Get counts from the classical register "meas".
print(
    f" >> First five results for the meas output register: "
    f"{pub_result.data.meas.get_bitstrings()[:5]}"
)

>> First five results for the meas output register: ['0110001001111000110000000111110011100111100001110100111011011011001111100011011010110011111000000110111000110000001110100110111', '1110001011100111100000001110010100010110001011110100111111110111100001100010010000011111100010000100000111100010011000000010111', '0111100101110001001011010111111110111010001001100011000111001101101010001011101101000010110010000011010101001011101110010100101', '0110110010001000010010000110000010000100111111101011111000010111010101000110001010100110100010110000000010101011000011111110110', '0001010111110100001010000011010010101110000101100011001000111111000010101111110100000011010000101111110110111110011010001001101']

Usar lotes y opciones avanzadas

Explora el modo de ejecución por lotes y las opciones avanzadas para optimizar el rendimiento del circuito en las QPUs.

import numpy as np
from qiskit.circuit.library import iqp
from qiskit.quantum_info import random_hermitian
from qiskit.transpiler import generate_preset_pass_manager
from qiskit_ibm_runtime import Batch, QiskitRuntimeService

# Legacy server-side primitive before qiskit-ibm-runtime 0.50.0
# from qiskit_ibm_runtime import Sampler

# Client-side primitive introduced in qiskit-ibm-runtime 0.50.0
from qiskit_ibm_runtime.executor_sampler import Sampler

n_qubits = 127

service = QiskitRuntimeService()
backend = service.least_busy(
    operational=True, simulator=False, min_num_qubits=n_qubits
)

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()

pm = generate_preset_pass_manager(backend=backend, optimization_level=1)
isa_circuit = pm.run(circuit)
another_isa_circuit = pm.run(another_circuit)

# The context manager automatically closes the batch.
with Batch(backend=backend) as batch:
    sampler = Sampler(mode=batch)
    job = sampler.run([isa_circuit])
    another_job = sampler.run([another_isa_circuit])
    result = job.result()
    another_result = another_job.result()

# first job

print(
    f" > The first five measurement results of job 1: "
    f"{result[0].data.meas.get_bitstrings()[:5]}"
)

> The first five measurement results of job 1: ['0110100010111000010001000100100011111000100001010001000110010101011000100101000111010000110001010000001001101110101101000100000', '1001100000101110011000000101010000001100110110000101100011010001000000001001001000000011110000001110000000001001000000010000010', '0110000001010111011110011100010101101010011100000100001000110100010110100101111111000000010010001110100000000000000001100001100', '0000010011100000010111010111010100100100010000110110111111010111001010111101100010000100101011000000000010101110000011010001000', '0110000101011001001001111101011001111001100101000111010000001100111001000111111100000010001001110110010100110011000000000110110']

# second job
print(
    " > The first five measurement results of job 2:",
    another_result[0].data.meas.get_bitstrings()[:5],
)

> The first five measurement results of job 2: ['1001111000100001101101011000000001101101100101110001010110100000100111001011011110011010101001001010100100110001010000001000001', '1000111101000111011110110000000100010100011111110110001001101000001111111010001101010010111010000101101000001110100000110010001', '0101001101000011011000010101100101011110010010011000111100000000010010010010000001100000001001001000000010011110100011111100100', '0010110010110011100011001010100100011100001010001100110100000000111100111000000100011101111011011000101001000010100010101111000', '0011110001100010010111010010100110110010011000010000100100100000111011000011001001110100000110001010101000011000110001001101100']

Próximos pasos

Recomendaciones

• Especifica opciones avanzadas de runtime.
• Practica con las primitivas trabajando en la lección de función de costo en IBM Quantum Learning.
• Aprende a transpilar localmente en la sección Transpilar.
• Prueba la guía Comparar configuraciones del transpilador.
• Comprende los Límites de trabajos al enviar un trabajo a una QPU de IBM®.