샘플 예시

이 페이지의 코드는 다음 요구 사항을 바탕으로 개발되었습니다. 이 버전 이상을 사용하시기를 권장합니다.
```
qiskit[all]~=2.5.2
qiskit-ibm-runtime~=0.47.0
```

양자 회로 출력값에서 샘플링된, 오류가 완화된 전체 준확률 분포를 생성합니다. Sampler의 기능을 활용하여 Grover’s 및 QVSM과 같은 검색 및 분류 알고리즘을 구현하십시오.

단일 실험 실행

Sampler를 사용하여 단일 회로의 측정 결과를 비트열 또는 카운트 값으로 반환합니다.

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]}")

Output:

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

단일 작업에서 여러 실험을 실행합니다

Sampler를 사용하여 하나의 작업 내에서 여러 회로의 측정 결과를 비트열 또는 카운트 값으로 반환합니다.

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]}"
    )

Output:

 > 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']

매개변수화된 회로 실행

단일 작업 내에서 여러 실험을 수행하고, 매개변수 값을 활용하여 회로의 재사용성을 높입니다.

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 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]}"
)

Output:

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

일괄 처리 및 고급 옵션 사용

QPU에서 회로 성능을 최적화하기 위해 일괄 실행 모드와 고급 옵션을 살펴보세요.

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]}"
)

Output:

 > 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],
)

Output:

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

다음 단계

권장사항

고급 런타임 옵션을 지정합니다.
IBM Quantum Learning 의 ‘Cost’ 함수 강의를 따라가며 기본형(primitives)을 연습해 보세요.
‘트랜스파일’ 섹션에서 로컬 환경에서 트랜스파일하는 방법을 알아보세요.
‘트랜스파일러 설정 비교’ 가이드를 확인해 보세요.
IBM® QPU로 작업을 전송할 때 작업 제한 사항을 숙지하십시오.

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