Exemples de primitives
Nouveau modèle d'exécution, actuellement en version bêta
La version bêta d'un nouveau modèle d'exécution est désormais disponible. Le modèle d'exécution dirigée offre plus de flexibilité pour personnaliser ton flux de travail d'att énuation des erreurs. Consulte le guide Modèle d'exécution dirigée pour plus d'informations.
Versions des packages
Le code de cette page a été développé avec les dépendances suivantes. Nous recommandons d'utiliser ces versions ou des versions plus récentes.
qiskit[all]~=2.3.0
qiskit-ibm-runtime~=0.43.1
Les exemples de cette section illustrent quelques façons courantes d'utiliser les primitives. Avant d'exécuter ces exemples, suis les instructions de la page Installer et configurer.
remarque
Ces exemples utilisent tous les primitives de Qiskit Runtime, mais tu pourrais utiliser les primitives de base à la place.
Exemples avec Estimator
Calcule et interprète efficacement les valeurs d'espérance des opérateurs quantiques requis par de nombreux algorithmes grâce à Estimator. Explore ses usages en modélisation moléculaire, en apprentissage automatique et dans des problèmes d'optimisation complexes.
Exécuter une seule expérience
Utilise Estimator pour déterminer la valeur d'espérance d'une paire circuit-observable unique.
# 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 SparsePauliOp, random_hermitian
from qiskit_ibm_runtime import QiskitRuntimeService, EstimatorV2 as Estimator
n_qubits = 50
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)
observable = SparsePauliOp("Z" * 50)
pm = generate_preset_pass_manager(backend=backend, optimization_level=1)
isa_circuit = pm.run(circuit)
isa_observable = observable.apply_layout(isa_circuit.layout)
estimator = Estimator(mode=backend)
job = estimator.run([(isa_circuit, isa_observable)])
result = job.result()
print(f" > Expectation value: {result[0].data.evs}")
print(f" > Metadata: {result[0].metadata}")
> Expectation value: -0.13582342954159593
> Metadata: {'shots': 4096, 'target_precision': 0.015625, 'circuit_metadata': {}, 'resilience': {}, 'num_randomizations': 32}
Exécuter plusieurs expériences dans un seul job
Utilise Estimator pour déterminer les valeurs d'espérance de plusieurs paires circuit-observable.
import numpy as np
from qiskit.circuit.library import iqp
from qiskit.transpiler import generate_preset_pass_manager
from qiskit.quantum_info import SparsePauliOp, random_hermitian
from qiskit_ibm_runtime import QiskitRuntimeService, EstimatorV2 as Estimator
n_qubits = 50
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)]
pubs = []
circuits = [iqp(mat) for mat in mats]
observables = [
SparsePauliOp("X" * 50),
SparsePauliOp("Y" * 50),
SparsePauliOp("Z" * 50),
]
# Get ISA circuits
pm = generate_preset_pass_manager(optimization_level=1, backend=backend)
for qc, obs in zip(circuits, observables):
isa_circuit = pm.run(qc)
isa_obs = obs.apply_layout(isa_circuit.layout)
pubs.append((isa_circuit, isa_obs))
estimator = Estimator(backend)
job = estimator.run(pubs)
job_result = job.result()
for idx in range(len(pubs)):
pub_result = job_result[idx]
print(f">>> Expectation values for PUB {idx}: {pub_result.data.evs}")
print(f">>> Standard errors for PUB {idx}: {pub_result.data.stds}")
>>> Expectation values for PUB 0: 0.4873096446700508
>>> Standard errors for PUB 0: 1.3528950031716114
>>> Expectation values for PUB 1: -0.00390625
>>> Standard errors for PUB 1: 0.015347884419435263
>>> Expectation values for PUB 2: -0.02001953125
>>> Standard errors for PUB 2: 0.013797455737635134
Exécuter des circuits paramétrés
Utilise Estimator pour exécuter trois expériences dans un seul job, en tirant parti des valeurs de paramètres pour accroître la réutilisabilité des circuits.
import numpy as np
from qiskit.circuit import QuantumCircuit, Parameter
from qiskit.quantum_info import SparsePauliOp
from qiskit.transpiler import generate_preset_pass_manager
from qiskit_ibm_runtime import QiskitRuntimeService, EstimatorV2 as Estimator
service = QiskitRuntimeService()
backend = service.least_busy(operational=True, simulator=False)
# Step 1: Map classical inputs to a quantum problem
theta = Parameter("θ")
chsh_circuit = QuantumCircuit(2)
chsh_circuit.h(0)
chsh_circuit.cx(0, 1)
chsh_circuit.ry(theta, 0)
number_of_phases = 21
phases = np.linspace(0, 2 * np.pi, number_of_phases)
individual_phases = [[ph] for ph in phases]
ZZ = SparsePauliOp.from_list([("ZZ", 1)])
ZX = SparsePauliOp.from_list([("ZX", 1)])
XZ = SparsePauliOp.from_list([("XZ", 1)])
XX = SparsePauliOp.from_list([("XX", 1)])
ops = [ZZ, ZX, XZ, XX]
# Step 2: Optimize problem for quantum execution.
pm = generate_preset_pass_manager(backend=backend, optimization_level=1)
chsh_isa_circuit = pm.run(chsh_circuit)
isa_observables = [
operator.apply_layout(chsh_isa_circuit.layout) for operator in ops
]
# Step 3: Execute using Qiskit primitives.
# Reshape observable array for broadcasting
reshaped_ops = np.fromiter(isa_observables, dtype=object)
reshaped_ops = reshaped_ops.reshape((4, 1))
estimator = Estimator(backend, options={"default_shots": int(1e4)})
job = estimator.run([(chsh_isa_circuit, reshaped_ops, individual_phases)])
# Get results for the first (and only) PUB
pub_result = job.result()[0]
print(f">>> Expectation values: {pub_result.data.evs}")
print(f">>> Standard errors: {pub_result.data.stds}")
print(f">>> Metadata: {pub_result.metadata}")
>>> Expectation values: [[ 1.0455093 0.98152862 0.82113463 0.60354133 0.29572641 0.01149883
-0.33110743 -0.60560522 -0.83322315 -0.96531231 -1.0257549 -0.95853095
-0.81081517 -0.61091237 -0.30221293 0.0035381 0.31371176 0.61061753
0.83646641 0.97091431 1.03135689]
[ 0.03390682 0.31194271 0.620937 0.87391133 0.96973494 1.03872794
0.94260949 0.82378821 0.56344283 0.28688115 -0.04570049 -0.37474403
-0.64540887 -0.87803912 -0.97887504 -1.03577952 -0.97268336 -0.83970967
-0.59705481 -0.29867482 0.0380346 ]
[ 0.00265358 -0.32992806 -0.59646512 -0.80934096 -0.96737621 -1.00128302
-0.94673728 -0.82703147 -0.59705481 -0.31341692 -0.00117937 0.29985419
0.59469607 0.78486908 0.93346939 0.97622146 0.94732696 0.81199454
0.60914332 0.28393273 -0.00678136]
[ 0.99656555 0.93553328 0.78398456 0.55872536 0.29749546 -0.04511081
-0.33523522 -0.62889773 -0.82201916 -0.95351864 -1.02634458 -0.96796589
-0.82054495 -0.57553135 -0.30103356 0.00265358 0.3104685 0.59705481
0.83322315 0.94437854 0.99214292]]
>>> Standard errors: [[0.014353 0.01441151 0.01620648 0.0195418 0.019762 0.01515649
0.02102523 0.02112359 0.0148494 0.01119219 0.01576623 0.01245824
0.01239832 0.01501273 0.01821305 0.01776286 0.01500156 0.01635231
0.01577367 0.01315371 0.01089558]
[0.01352805 0.01627835 0.01247646 0.01287866 0.01570182 0.01060924
0.01590468 0.01620303 0.01530626 0.01619973 0.01918078 0.01379676
0.01564971 0.01377673 0.01454324 0.01242184 0.01252201 0.01396738
0.01326188 0.0145736 0.01795044]
[0.02029376 0.01610892 0.0161542 0.0157785 0.01385665 0.01113743
0.01375237 0.01380922 0.0145974 0.01759484 0.01594193 0.02111719
0.01521368 0.01365888 0.01188512 0.01353009 0.01195674 0.01446547
0.01660987 0.01511225 0.01880871]
[0.01105161 0.01164476 0.01329858 0.01439545 0.01888747 0.01629201
0.01405852 0.01406643 0.01088709 0.01275198 0.01281432 0.01333301
0.01268483 0.01443594 0.01495655 0.01715532 0.01822699 0.01508936
0.01435528 0.01340555 0.01295649]]
>>> Metadata: {'shots': 10016, 'target_precision': 0.01, 'circuit_metadata': {}, 'resilience': {}, 'num_randomizations': 32}
Utiliser les sessions et les options avancées
Explore les sessions et les options avancées pour optimiser les performances des circuits sur les QPU.
attention
Le bloc de code suivant renverra une erreur pour les utilisateurs du plan Open, car il utilise des sessions. Les charges de travail du plan Open ne peuvent s'exécuter qu'en mode job ou en mode batch.
import numpy as np
from qiskit.circuit.library import iqp
from qiskit.transpiler import generate_preset_pass_manager
from qiskit.quantum_info import SparsePauliOp, random_hermitian
from qiskit_ibm_runtime import (
QiskitRuntimeService,
Session,
EstimatorV2 as Estimator,
)
n_qubits = 50
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)
mat = np.real(random_hermitian(n_qubits, seed=rng))
another_circuit = iqp(mat)
observable = SparsePauliOp("X" * 50)
another_observable = SparsePauliOp("Y" * 50)
pm = generate_preset_pass_manager(optimization_level=1, backend=backend)
isa_circuit = pm.run(circuit)
another_isa_circuit = pm.run(another_circuit)
isa_observable = observable.apply_layout(isa_circuit.layout)
another_isa_observable = another_observable.apply_layout(
another_isa_circuit.layout
)
with Session(backend=backend) as session:
estimator = Estimator(mode=session)
estimator.options.resilience_level = 1
job = estimator.run([(isa_circuit, isa_observable)])
another_job = estimator.run(
[(another_isa_circuit, another_isa_observable)]
)
result = job.result()
another_result = another_job.result()
# first job
print(f" > Expectation value: {result[0].data.evs}")
print(f" > Metadata: {result[0].metadata}")
# second job
print(f" > Another Expectation value: {another_result[0].data.evs}")
print(f" > More Metadata: {another_result[0].metadata}")
> Expectation value: 0.08045977011494253
> Metadata: {'shots': 4096, 'target_precision': 0.015625, 'circuit_metadata': {}, 'resilience': {}, 'num_randomizations': 32}
> Another Expectation value: 0.02127659574468085
> More Metadata: {'shots': 4096, 'target_precision': 0.015625, 'circuit_metadata': {}, 'resilience': {}, 'num_randomizations': 32}
Exemples avec Sampler
Génère des distributions quasi-probabilistes complètes avec atténuation des erreurs, échantillonnées depuis les sorties de circuits quantiques. Exploite les capacités de Sampler pour des algorithmes de recherche et de classification tels que Grover et QVSM.
Exécuter une seule expérience
Utilise Sampler pour obtenir les résultats de mesure sous forme de chaînes de bits ou de comptages pour un circuit unique.
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 dans un seul job
Utilise Sampler pour obtenir les résultats de mesure sous forme de chaînes de bits ou de comptages pour 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 ten results for pub {idx}: {pub_result.data.meas.get_bitstrings()[:10]}"
)
> 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 dans un seul job, en tirant parti des valeurs de paramètres pour accroître 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 ten results for the meas output register: {pub_result.data.meas.get_bitstrings()[:10]}"
)
>> First ten results for the meas output register: ['1100011011100001011000001001000001111110000001011100011110011100111110000111000100011100001111100010010111110001001111011000101', '1100011101010101010000100110110110010001100101011101001011101010111110000111110100000011111010101101011101101101001111011110011', '0000000011000011001101001000111110001100010010011011001111000101000000001111111101101011100111010110111101010111011001010001011', '0101010001101110100010001100111001011101101100001000100001011101110100001000011011001011110101000110010001001010011011100011101', '0110101110000010110000001000010101100010010001001001101000010100110001011111110001000001100110010001011111001010011001001000101', '0111011111110111010111100110101000010100101000001010001001011111010010100111110110000011100001100000110000111000011011100000000', '0110100111001000100100110110010001011110000000110111000011110000100111001000100110011100100001100000101111111100010111100111001', '0101101111010110000000001000010110100101001100001101110010101111010110001010000111010010001111000000011001001001111100111010110', '0100000110010101111011110111000010001101011110010000110010001111001101010010000011111100100101101000010000111100111010000000110', '0011110110011011000110000100100110111000000010010101111011111000111001100011110100001100010100100001110101110100011100110001100']
Utiliser les sessions et les options avancées
Explore les sessions et les options avancées pour optimiser les performances des circuits sur les QPU.
attention
Le bloc de code suivant renverra une erreur pour les utilisateurs du plan Open, car il utilise des sessions. Les charges de travail du plan Open ne peuvent s'exécuter qu'en mode job ou en mode batch.
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 Session, 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)
with Session(backend=backend) as session:
sampler = Sampler(mode=session)
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 ten measurement results of job 1: {result[0].data.meas.get_bitstrings()[:10]}"
)
> The first ten measurement results of job 1: ['1101100010101100001001110000100011110011000010110000010000001011000110000110010100011000101111011110010101101001000101010100010', '0010100100011100001011111101001010010000010010000100000011001010001110101011010000100011001010000101110101110110010000110001110', '1011000110110011011010001111001011111000011111111010010010011000000110000001000101001111001000010110000000011101010000111101101', '0101000010000101001011111010110011101000100101010011001000010000011010000010101000000001000100010100011100101001000101001011000', '1101010101011100000001100110111001000100110011110001110011000000110100011011100000010000001100001101011000000001010101001101001', '1111100011111010000000100011100110101000010101100100000110000110001011100000000101010110011110010010000100011110000010101010100', '1011011100110001000110100100110010101101110010100010011100001000001100010101101110010101100000001110000000111001001000000100010', '0100011011110111010010111011101010111010010011011110011001000010101110100100111010110001101100110001010100000101001000000111001', '0001110001110000001011101101010001001110000010100001000101100100110111001011100000101010011100011001110011100100000000010110001', '1010110110111000001100011100000100101000000001111110110010000110011100100100100010000101111110100110010010010101001011001000011']
# second job
print(
" > The first ten measurement results of job 2:",
another_result[0].data.meas.get_bitstrings()[:10],
)
> The first ten measurement results of job 2: ['0100010001111001111010000100101010011010000100010110100100010010010110001010101010000000110000010000001100100011000110101000001', '1101000100010000011100110101001110101100001000000000101001110110110010110110010010011100010000010001011000011100100000100000000', '1111101010100011010100000100010101111110011000000000010000010000101001010001100000100000100010000001100111000000111000111010000', '0101111100000110010101101100101110101011010100001001110101100010111100110011100001110101000000001000000000101000100000001000000', '1101001000000000011000010100111110101111001001110011100001100100100100000011110001001000001000010101111100001001110010110011100', '1100001000110110000111110110010010000100001000001001100011110001111100100101110010010111010010101100001010101011100100001010010', '0001001100010000000101101101101111000011101100101000111010000000000010010111011000100000011010100000100011100010110010010000001', '1010101100000000011000111101000011100101000110110000111111000001100010001110000101111111110110000000000000001000000010001110000', '1111111001001001001100010000101110110100001011011100010001100000100001010100111011000110100011110000001010101000010000000011000', '1011011010101100010101100001001000000010110001101000100001111010000100011100000000100111001001000001001001101000001000100000000']
Étapes suivantes
Recommandations
- Spécifier des options avancées d'exécution.
- Entraîne-toi avec les primitives en suivant la leçon sur les fonctions de coût dans IBM Quantum Learning.
- Apprends à transpiler localement dans la section Transpiler.
- Essaie le guide Comparer les paramètres du transpilateur.
- Lis Migrer vers les primitives V2.
- Comprends les limites de jobs lors de l'envoi d'un job à un QPU IBM®.