ตัวอย่าง primitives (ส่วนที่ 1)

โมเดลการรันใหม่ ขณะนี้อยู่ในช่วง beta

beta release ของโมเดลการรันใหม่พร้อมให้ใช้งานแล้ว โมเดล directed execution มอบความยืดหยุ่นมากขึ้นในการปรับแต่งขั้นตอน error mitigation ดูข้อมูลเพิ่มเติมได้ที่คู่มือ Directed execution model

Package versions

The code on this page was developed using the following requirements. We recommend using these versions or newer.

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

ตัวอย่างในส่วนนี้แสดงวิธีการทั่วไปในการใช้ primitives ก่อนรันตัวอย่างเหล่านี้ ให้ทำตามคำแนะนำใน Install and set up.

หมายเหตุ

ตัวอย่างทั้งหมดใช้ primitives จาก Qiskit Runtime แต่สามารถใช้ base primitives แทนได้เช่นกัน

ตัวอย่าง Estimator

คำนวณและตีความ expectation value ของ quantum operator ที่จำเป็นสำหรับ algorithm หลายๆ ตัวอย่างมีประสิทธิภาพด้วย Estimator สำรวจการใช้งานในการสร้างแบบจำลองโมเลกุล machine learning และปัญหาการออปติไมซ์ที่ซับซ้อน

รันการทดลองเดียว

ใช้ Estimator เพื่อหา expectation value ของคู่ circuit-observable เดียว

# 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}

รันหลายการทดลองใน job เดียว

ใช้ Estimator เพื่อหา expectation value ของคู่ circuit-observable หลายคู่

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

รัน circuit แบบ parameterized

ใช้ Estimator เพื่อรันสามการทดลองใน job เดียว โดยใช้ค่าพารามิเตอร์เพื่อเพิ่มความสามารถในการนำ circuit กลับมาใช้ซ้ำ

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}

ใช้ Session และตัวเลือกขั้นสูง

สำรวจ Session และตัวเลือกขั้นสูงเพื่อออปติไมซ์ประสิทธิภาพ circuit บน QPU

ข้อควรระวัง

โค้ดบล็อคต่อไปนี้จะคืนค่า error สำหรับผู้ใช้แผน Open Plan เนื่องจากใช้ Session workload บน Open Plan สามารถรันได้เฉพาะใน job mode หรือ batch mode เท่านั้น

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}

title: "ตัวอย่าง primitives (ส่วนที่ 2)" sidebar_label: "ตัวอย่าง primitives (ส่วนที่ 2)" description: "ตัวอย่างการใช้งาน Sampler primitives ใน Qiskit Runtime ในทางปฏิบัติ" notebook_path: "guides/primitives-examples.ipynb"

ตัวอย่าง Sampler

สร้างการกระจาย quasi-probability ทั้งหมดที่ลด error แล้วซึ่งสุ่มตัวอย่างจาก quantum circuit output ใช้ความสามารถของ Sampler สำหรับ algorithm การค้นหาและจัดหมวดหมู่ เช่น Grover's และ QVSM

รันการทดลองเดียว

ใช้ Sampler เพื่อคืนผลลัพธ์การวัดเป็น bitstring หรือ count ของ circuit เดียว

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']

รันหลายการทดลองใน job เดียว

ใช้ Sampler เพื่อคืนผลลัพธ์การวัดเป็น bitstring หรือ count ของ circuit หลายตัวใน job เดียว

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']

รัน circuit แบบ parameterized

รันหลายการทดลองใน job เดียว โดยใช้ค่าพารามิเตอร์เพื่อเพิ่มความสามารถในการนำ circuit กลับมาใช้ซ้ำ

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']

ใช้ Session และตัวเลือกขั้นสูง

สำรวจ Session และตัวเลือกขั้นสูงเพื่อออปติไมซ์ประสิทธิภาพ circuit บน QPU

ข้อควรระวัง

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']

ขั้นตอนถัดไป

คำแนะนำ

ระบุตัวเลือก runtime ขั้นสูง
ฝึกกับ primitives ด้วยการเรียน Cost function lesson ใน IBM Quantum Learning
เรียนรู้วิธี transpile ในเครื่องในส่วน Transpile
ลอง Compare transpiler settings
อ่าน Migrate to V2 primitives
ทำความเข้าใจ Job limits เมื่อส่ง job ไปยัง IBM® QPU

Source: IBM Quantum docs — updated 27 เม.ย. 2569

English version on doQumentation — updated 7 พ.ค. 2569

This translation based on the English version of 11 มี.ค. 2569

ตัวอย่าง Estimator​

รันการทดลองเดียว​

รันหลายการทดลองใน job เดียว​

รัน circuit แบบ parameterized​

ใช้ Session และตัวเลือกขั้นสูง​

ตัวอย่าง Sampler​

รันการทดลองเดียว​

รันหลายการทดลองใน job เดียว​

รัน circuit แบบ parameterized​

ใช้ Session และตัวเลือกขั้นสูง​

ขั้นตอนถัดไป​

ตัวอย่าง Estimator

รันการทดลองเดียว

รันหลายการทดลองใน job เดียว

รัน circuit แบบ parameterized

ใช้ Session และตัวเลือกขั้นสูง

ตัวอย่าง Sampler

รันการทดลองเดียว

รันหลายการทดลองใน job เดียว

รัน circuit แบบ parameterized

ใช้ Session และตัวเลือกขั้นสูง

ขั้นตอนถัดไป