Exemples Sampler

Versions des packages

Le code sur cette page a été développé en utilisant les exigences suivantes. Nous recommandons d'utiliser ces versions ou des versions plus récentes.

qiskit[all]~=2.3.0
qiskit-ibm-runtime~=0.43.1

Génère des distributions quasi-probabilistes entières atténuées des erreurs échantillonnées à partir des sorties de circuits quantiques. Tire parti des capacités de Sampler pour les algorithmes de recherche et de classification comme Grover et QVSM.

Exécuter une seule expérience

Utilise Sampler pour renvoyer le résultat de mesure sous forme de chaînes de bits ou de comptes d'un seul circuit.

# Added by doQumentation — required packages for this notebook
!pip install -q numpy qiskit qiskit-ibm-runtime

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, SamplerV2 as 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: ['0101110000110001001111000101001111000110110100011000100101011101110011010010010101000110000111101010101000001010000100100000100', '0100010101111101010000100010011100110001010000011000000010001100010111000011001010000100100000100000000010000000010010101011110', '1101010111111111100010000011101010101010100100011001000000001001110010001000000010000010000101000111000100010010000001111000010', '1001110001100001001101111010111100000100010110010001001100111000110010111000001010001000000000000000100101101001110010101000110', '0001000000011011000011000111001000000000100110110011111110110100110000101010100010000010101011011000101011101000100000110000011', '1011100010011111010000001110110000111101000001110010011001100011111010001100100000110001000010001010110011100010000111000111010', '1101110000011000001011011000001111001110010111111111100100010001110100000010000001011000110000000011010011110100101001101000010', '0110100000110011000011001000110110110001000100100001111010001101000001010111000000101010101000001110100100001010110001000100101', '1000011010011011001111010010100000001110010010100000011010000110011010100000111000010010100111000001100101100010110010101001010', '1011011100111001010010101001000111000001110011110011001111010100100011101111011101011000000111011010000011100011010000001000000']

Exécuter plusieurs expériences en un seul job

Utilise Sampler pour renvoyer le résultat de mesure sous forme de chaînes de bits ou de comptes de plusieurs circuits en un seul job.

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, SamplerV2 as 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 ten results for pub 0: ['1000000101000100010111001010101010000001001010101011011001011110001000000110110101010000010000000000110001001000011111110000001', '1111101011011011110001011000001100001101100001000101111011101110000101111010001011111010001001000010111001110111000010001011010', '1100100101110010000110101011110010111001101010001101100010110100110110000110110010001110001000001010011100001000011011000111010', '1100010010000100100010110100011010011001010101101101101001100001001110011001011011111100011100100001000101010000111101110001101', '0011101011101100010011111001001110000101100110000110000001111000011010011110000110100000110011011000000010110001010000111000100', '0110101101110000010110100100010011000100100010000010010010110001111111110000101011000100010000000100100100110011010111101110111', '1101011000111100011000010110000010001100101011000001110010110001111101010101011110110010000100011101000001010110010101000000100', '0000101010010100000010111110111000001011000000001011000110100010110011111000110110010110011010111101001011000000001101001110110', '1100101000110001000011111110010001011000010110010101101000000101011110000100011011111011011010001001110011011101001101010100000', '0110011000101110101001010100110010101000010111100001000111011000110101011010010101110011001010101000001001001000110010100010101']
 > First ten results for pub 1: ['1100100001011010010100000110101010100111101100110000100001011000100010001101010101101110000011010010011000010000010001000001000', '1100000011000000100110011000000110010000011111000000001010000101000010011001000001010000001000001010001000110010111000010000000', '0010000111101000111010101010101001010000001110100001011011100011000111000000010101001000010101001100000010100010011000000000010', '0010100100001000011100001010011000001010000010001000000001011100001010001110010110111101101000001101010101000000000011000100110', '0101101000011110111000100010000000101110100001010101110010001100001100001000111111110101001010100110000000010011111111000000010', '0101010111000000001110100110100011010111000111110100010010010001011010001000101001100001100110001001001000010010000011100100000', '0110010000001110111010010100010010010011010010110101001110010010001001101010111000010000000100011001001000001111010001100010010', '1100001100101011011010000110111110001101010100010100101100111000010000101101101010111011111011101100000000110000100101001000101', '0000111100001000000101101001010111110100011011011101101111000000001010001001100010110000100000000001010100110001001100110010000', '0100100001001011110000110001100001111011111100000001010111011011100010110111101110101111101010100101000000110111000110000000000']
 > First ten results for pub 2: ['1000010100111010101010111110101000110101010001111110011110011001010100001100100000000001000111111011001101100001001110011101100', '1110100000111000000000110110010100000011110000011110000110100010000100001100010101101001100100010111000010100101011000001000000', '1000010111011000000001110111010101000111111010010011110100001010000000111111100100001111111101010100001001011100111101010000010', '0000111011110110010011100111001010001000011010010110010010101000101110011100000010000101011000101001001001000100111101010100100', '0100000100111101110000101111011000100111101011101110100001000001000010101111100100000111010001101001100001100011011110101101100', '0100001000110101010010010100100110000100001010100001110001110101010011000111100111001001100000010100110111010111010100010100100', '0011111000010001101100000110111001000000100111110100001100001100010010010101011000000111011011111010100010000100100000100000000', '1000010010101100110110110110100010100000111001101011110100001000011000001000000110010001001011100100000000100000000000000000000', '0001011100010011111110011110000001000000010100111111000000101010000011011110110000110001010010000010010001000101110001111100010', '1111010100011100010010010110000101110000010001100101011111001100010111100001011001000001011010111011100001000001100000000000110']

Exécuter des circuits paramétrés

Exécute plusieurs expériences en un seul job, en tirant parti des valeurs de paramètres pour augmenter la réutilisabilité des circuits.

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, SamplerV2 as 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 Qiskit 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 ten results for the meas output register: ['1100011011100001011000001001000001111110000001011100011110011100111110000111000100011100001111100010010111110001001111011000101', '1100011101010101010000100110110110010001100101011101001011101010111110000111110100000011111010101101011101101101001111011110011', '0000000011000011001101001000111110001100010010011011001111000101000000001111111101101011100111010110111101010111011001010001011', '0101010001101110100010001100111001011101101100001000100001011101110100001000011011001011110101000110010001001010011011100011101', '0110101110000010110000001000010101100010010001001001101000010100110001011111110001000001100110010001011111001010011001001000101', '0111011111110111010111100110101000010100101000001010001001011111010010100111110110000011100001100000110000111000011011100000000', '0110100111001000100100110110010001011110000000110111000011110000100111001000100110011100100001100000101111111100010111100111001', '0101101111010110000000001000010110100101001100001101110010101111010110001010000111010010001111000000011001001001111100111010110', '0100000110010101111011110111000010001101011110010000110010001111001101010010000011111100100101101000010000111100111010000000110', '0011110110011011000110000100100110111000000010010101111011111000111001100011110100001100010100100001110101110100011100110001100']

Utiliser les lots et les options avancées

Explore le mode d'exécution par lots et les options avancées pour optimiser les performances des circuits sur les QPU.

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, SamplerV2 as Sampler
from qiskit_ibm_runtime import QiskitRuntimeService

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: ['1001111000111001100010010111000111101101000000101000010010101001110000000010001110010001011000100100000101010010000001001000010', '0000010000001101010100011001001011011010000110000100011000000000011000010111101100010101011100100101000110011000110000000000011', '0001100011110010110100110010111001001110101100100010011001100100111000110011000100000000100001001001100100101010110010000111101', '1000100111000111010011111010010111011001100000001001101010100001101010010110100100001010000000101110100010000000100110001100000', '1011011111101100000001001010111100001001111010000000001011001000011010000101110000101010101011000110110011010011011000010000001']

# 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: ['1100111000010110001000101110100001011010101100101001111000100100010101111111001000000000000000110000001100110001001000000010000', '0011010001111000001011011010011000110010101111001100000000011110011011110010010011010000000010010011001011001110010001000100100', '0101001001101010011010011000001001010000001111001100001001011100110001001001001110100001101000000101000001000011000000000110100', '0100010010100000101000001001100010000110100111010000101010010110111111110010000011001110000001100000001011000000000100000000001', '1101000000001110110101011000011111111101011101100010000001011010010001110100001010001010010110100010000010100011000000010100100']

Étapes suivantes

Recommandations

• Spécifier des options d'exécution avancées.
• Pratique avec les primitives en suivant la leçon sur la fonction de coût dans IBM Quantum Learning.
• Apprends à transpiler localement dans la section Transpilation.
• Essaie le guide Comparer les paramètres du Transpiler.
• Lis Migrer vers les primitives V2.
• Comprends les limites de job lors de l'envoi d'un job à une QPU IBM®.