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패키지 버전

이 페이지의 코드는 다음 요구 사항을 사용하여 개발되었습니다. 다음 버전 이상을 사용하는 것이 좋습니다.

qiskit[all]~=2.1.1
qiskit-ibm-runtime~=0.40.1

이 예제에는 두 부분이 포함되어 있습니다. 먼저 간단한 양자 프로그램을 만들어 양자 처리 장치(QPU)에서 실행합니다. 실제 양자 연구에는 훨씬 더 강력한 프로그램이 필요하므로 두 번째 섹션 (많은 수의 큐비트로 확장 )에서는 간단한 프로그램을 유틸리티 수준까지 확장합니다.

설치 및 인증

아직 키스킷을 설치하지 않았다면 빠른 시작 가이드에서 지침을 확인하세요.
- 퀀텀 하드웨어에서 작업을 실행하려면 Qiskit Runtime 을 설치하세요:
```
pip install qiskit-ibm-runtime
```
- 로컬에서 Jupyter 노트북을 실행할 환경을 설정하세요:
```
pip install jupyter
```
무료 오픈 요금제를 통해 양자 하드웨어에 액세스하기 위한 인증을 설정하세요.

(계정 가입 초대를 이메일로 받은 경우에는 초대받은 사용자를 위한 단계를 따르세요.)
- 로 이동하여 IBM Quantum Platform 로 이동하여 로그인하거나 계정을 만드세요.
- 대시보드에서 API 키( API 토큰이라고도 함)를 생성한 다음 안전한 위치에 복사합니다.
- 인스턴스 페이지로 이동하여 사용하려는 인스턴스를 찾습니다. CRN 위로 마우스를 가져가서 클릭하여 복사합니다.
- 이 코드를 사용하여 자격 증명을 로컬에 저장하세요:
```
from qiskit_ibm_runtime import QiskitRuntimeService
 
QiskitRuntimeService.save_account(
token="<your-api-key>", # Use the 44-character API_KEY you created and saved from the IBM Quantum Platform Home dashboard
instance="<CRN>", # Optional
)
```

이제 Qiskit Runtime 서비스에 인증하고 싶을 때 언제든지 이 Python 코드를 사용할 수 있습니다:

    from qiskit_ibm_runtime import QiskitRuntimeService
 
    # Run every time you need the service
    service = QiskitRuntimeService()

신뢰할 수 있는 Python 환경을 사용하지 않으시나요?

공용 컴퓨터나 기타 보안되지 않은 환경을 사용하는 경우 인증 자격 증명을 안전하게 유지하려면 수동 인증 지침을 따르세요.

간단한 양자 프로그램 생성 및 실행

키스킷 패턴을 사용하여 양자 프로그램을 작성하는 네 가지 단계는 다음과 같습니다:

문제를 퀀텀 네이티브 형식으로 매핑하세요.
회로와 연산자를 최적화하세요.
양자 프리미티브 함수를 사용하여 실행합니다.
결과를 분석하십시오.

1단계. 문제를 퀀텀 네이티브 형식으로 매핑하기

양자 프로그램에서 양자 회로는 양자 명령을 표현하는 기본 형식이며, 연산자는 측정할 관측값을 나타냅니다. 회로를 만들 때는 일반적으로 새 객체를 만든 다음 QuantumCircuit 객체를 생성한 다음 순차적으로 인스트럭션을 추가합니다.

다음 코드 셀은 두 큐비트가 서로 완전히 얽혀 있는 상태인 벨 상태를 생성하는 회로를 생성합니다.

참고: 비트 순서

Qiskit SDK는 LSb 0 비트 번호를 사용하며, 여기서 $n^{th}$ 숫자는 $1 \ll n$ 또는 $2^n$ 값을 가집니다. 자세한 내용은 Qiskit SDK 항목의 비트 순서를 참조하세요.

from qiskit import QuantumCircuit
from qiskit.quantum_info import SparsePauliOp
from qiskit.transpiler import generate_preset_pass_manager
from qiskit_ibm_runtime import EstimatorV2 as Estimator
 
# Create a new circuit with two qubits
qc = QuantumCircuit(2)
 
# Add a Hadamard gate to qubit 0
qc.h(0)
 
# Perform a controlled-X gate on qubit 1, controlled by qubit 0
qc.cx(0, 1)
 
# Return a drawing of the circuit using MatPlotLib ("mpl").
# These guides are written by using Jupyter notebooks, which
# display the output of the last line of each cell.
# If you're running this in a script, use `print(qc.draw())` to
# print a text drawing.
qc.draw("mpl")

Output:

문서에서 QuantumCircuit 문서에서 사용 가능한 모든 작업을 확인하세요.

양자 회로를 만들 때는 실행 후 어떤 유형의 데이터를 반환할지도 고려해야 합니다. 키스킷은 데이터를 반환하는 두 가지 방법을 제공합니다. 측정하기로 선택한 큐비트 집합에 대한 확률 분포를 구하거나 관측 가능성의 기대값을 구할 수 있습니다. 키스킷 프리미티브 ( 3단계에서 자세히 설명)를 사용하여 이 두 가지 방법 중 하나로 회로를 측정할 수 있도록 워크로드를 준비합니다.

이 예에서는 연산자(양자 상태를 변화시키는 동작이나 과정을 나타내는 데 사용되는 수학적 객체)를 사용하여 지정된 qiskit.quantum_info 서브모듈을 사용하여 기대값을 측정합니다. 다음 코드 셀은 6개의 2큐비트 폴리 연산자를 생성합니다: IZ, IX, ZI, XI, ZZ, XX 입니다.

# Set up six different observables.
 
observables_labels = ["IZ", "IX", "ZI", "XI", "ZZ", "XX"]
observables = [SparsePauliOp(label) for label in observables_labels]

연산자 표기법

여기서 ZZ 연산자는 텐서 곱 $Z\otimes Z$ 의 줄임말로, 큐비트 1의 Z와 큐비트 0의 Z를 함께 측정하고 큐비트 1과 큐비트 0의 상관관계에 대한 정보를 얻는 것을 의미합니다. 이와 같은 기대값은 일반적으로 $\langle Z_1 Z_0 \rangle$ 로도 작성됩니다.

상태가 얽힌 경우 $\langle Z_1 Z_0 \rangle$ 의 측정값은 $\langle I_1 \otimes Z_0 \rangle \langle Z_1 \otimes I_0 \rangle$ 의 측정값과 달라야 합니다. 위에서 설명한 회로에서 생성된 특정 얽힌 상태의 경우 $\langle Z_1 Z_0 \rangle$ 의 측정값은 1이 되어야 하고 $\langle I_1 \otimes Z_0 \rangle \langle Z_1 \otimes I_0 \rangle$ 의 측정값은 0이 되어야 합니다.

2단계. 회로 및 연산자 최적화

장치에서 회로를 실행할 때는 회로에 포함된 명령어 집합을 최적화하고 회로의 전체 깊이(대략적인 명령어 수)를 최소화하는 것이 중요합니다. 이렇게 하면 오류와 노이즈의 영향을 줄여 최상의 결과를 얻을 수 있습니다. 또한 회로의 명령어는 백엔드 디바이스의 명령어 집합 아키텍처(ISA) 를 준수해야 하며 디바이스의 기본 게이트와 큐비트 연결을 고려해야 합니다.

다음 코드는 작업을 제출할 실제 장치를 인스턴스화하고 해당 백엔드의 ISA와 일치하도록 회로와 관측값을 변환합니다. 자격 증명을 이미 저장해 두어야 합니다.

from qiskit_ibm_runtime import QiskitRuntimeService
 
service = QiskitRuntimeService()
 
backend = service.least_busy(simulator=False, operational=True)
 
# Convert to an ISA circuit and layout-mapped observables.
pm = generate_preset_pass_manager(backend=backend, optimization_level=1)
isa_circuit = pm.run(qc)
 
isa_circuit.draw("mpl", idle_wires=False)

Output:

3단계. 양자 프리미티브를 사용하여 실행하기

양자 컴퓨터는 무작위 결과를 생성할 수 있으므로 일반적으로 회로를 여러 번 실행하여 출력의 샘플을 수집합니다. Estimator 클래스를 사용하여 관찰 가능한 값을 추정할 수 있습니다. Estimator 는 두 가지 프리미티브 중 하나이며, 다른 하나는 Sampler 로 양자 컴퓨터에서 데이터를 가져오는 데 사용할 수 있습니다. 이러한 객체는 다음을 가지고 있습니다. run() 기본 통합 블록( PUB )을 사용하여 회로, 관측 가능 항목 및 매개변수(해당되는 경우)의 선택을 실행하는 방법입니다.

# Construct the Estimator instance.
 
estimator = Estimator(mode=backend)
estimator.options.resilience_level = 1
estimator.options.default_shots = 5000
 
mapped_observables = [
    observable.apply_layout(isa_circuit.layout) for observable in observables
]
 
# One pub, with one circuit to run against five different observables.
job = estimator.run([(isa_circuit, mapped_observables)])
 
# Use the job ID to retrieve your job data later
print(f">>> Job ID: {job.job_id()}")

Output:

>>> Job ID: d2qkdds65eic73blka9g

작업이 제출된 후에는 현재 파이썬 인스턴스 내에서 작업이 완료될 때까지 기다리거나 job_id 을 사용하여 나중에 데이터를 검색할 수 있습니다. (자세한 내용은 작업 검색하기 섹션을 참조하세요.)

작업이 완료된 후 작업의 result() 속성을 통해 출력을 확인합니다.

# This is the result of the entire submission.  You submitted one Pub,
# so this contains one inner result (and some metadata of its own).
job_result = job.result()
 
# This is the result from our single pub, which had six observables,
# so contains information on all six.
pub_result = job.result()[0]

대안: 시뮬레이터를 사용하여 예제 실행하기

실제 기기에서 양자 프로그램을 실행하면 워크로드가 실행되기 전에 대기열에서 대기해야 합니다. 시간을 절약하려면 대신 다음 코드를 사용하여 이 작은 워크로드를 fake_provider 에서 Qiskit Runtime 로컬 테스트 모드로 실행할 수 있습니다. 이는 소규모 회로에서만 가능하다는 점에 유의하세요. 다음 섹션에서 확장할 때는 실제 장치를 사용해야 합니다.

 
# Use the following code instead if you want to run on a simulator:
 
from qiskit_ibm_runtime.fake_provider import FakeFez
backend = FakeFez()
estimator = Estimator(backend)
 
# Convert to an ISA circuit and layout-mapped observables.
 
pm = generate_preset_pass_manager(backend=backend, optimization_level=1)
isa_circuit = pm.run(qc)
mapped_observables = [
    observable.apply_layout(isa_circuit.layout) for observable in observables
]
 
job = estimator.run([(isa_circuit, mapped_observables)])
result = job.result()
 
# This is the result of the entire submission.  You submitted one Pub,
# so this contains one inner result (and some metadata of its own).
 
job_result = job.result()
 
# This is the result from our single pub, which had five observables,
# so contains information on all five.
 
pub_result = job.result()[0]

4단계. 결과 분석

분석 단계에서는 일반적으로 측정 오류 완화 또는 영점 잡음 추정(ZNE) 등을 사용하여 결과를 후처리할 수 있습니다. 이러한 결과를 다른 워크플로우에 입력하여 추가 분석을 하거나 주요 값과 데이터의 도표를 준비할 수 있습니다. 일반적으로 이 단계는 문제에 따라 다릅니다. 이 예제에서는 회로에 대해 측정된 각 기대값을 플롯합니다.

추정기에 지정한 관측값에 대한 기대값과 표준 편차는 작업 결과의 PubResult.data.evs 및 PubResult.data.stds 속성을 통해 액세스할 수 있습니다. 샘플러에서 결과를 얻으려면 PubResult.data.meas.get_counts() 함수를 사용하면 dict 측정값을 키와 카운트로 된 비트스트링 형태로 반환합니다. 자세한 내용은 샘플러 시작하기를 참조 하세요

# Plot the result
 
from matplotlib import pyplot as plt
 
values = pub_result.data.evs
 
errors = pub_result.data.stds
 
# plotting graph
plt.plot(observables_labels, values, "-o")
plt.xlabel("Observables")
plt.ylabel("Values")
plt.show()

Output:

큐비트 0과 1의 경우 X와 Z의 독립 기대값은 모두 0이고 상관관계(XX 및 ZZ)는 1이라는 점에 유의하세요. 이것이 양자 얽힘의 특징입니다.

대량의 큐비트로 확장하기

양자 컴퓨팅 분야에서 진전을 이루기 위해서는 유틸리티 규모의 작업이 매우 중요합니다. 이러한 작업은 100개 이상의 큐비트와 1000개 이상의 게이트를 사용할 수 있는 회로로 작업하는 등 훨씬 더 큰 규모의 계산을 수행해야 합니다. 이 예는 100-큐비트 GHZ 상태를 생성하고 분석하여 IBM® QPU에서 유틸리티 규모 작업을 수행하는 방법을 보여줍니다. 키스킷 패턴 워크플로우를 사용하며 각 큐비트에 대한 기대값 $\langle Z_0 Z_i \rangle$ 을 측정하는 것으로 끝납니다.

1단계. 문제 매핑

$n$ -쿼비트 GHZ 상태(본질적으로 확장된 벨 상태)를 준비하는 QuantumCircuit 를 반환하는 함수를 작성한 다음, 이 함수를 사용하여 100-쿼비트 GHZ 상태를 준비하고 측정할 관측값을 수집합니다.

from qiskit import QuantumCircuit
 
 
def get_qc_for_n_qubit_GHZ_state(n: int) -> QuantumCircuit:
    """This function will create a qiskit.QuantumCircuit (qc) for an n-qubit GHZ state.
 
    Args:
        n (int): Number of qubits in the n-qubit GHZ state
 
    Returns:
        QuantumCircuit: Quantum circuit that generate the n-qubit GHZ state, assuming all qubits start in the 0 state
    """
    if isinstance(n, int) and n >= 2:
        qc = QuantumCircuit(n)
        qc.h(0)
        for i in range(n - 1):
            qc.cx(i, i + 1)
    else:
        raise Exception("n is not a valid input")
    return qc
 
 
# Create a new circuit with two qubits (first argument) and two classical
# bits (second argument)
n = 100
qc = get_qc_for_n_qubit_GHZ_state(n)

다음으로 관심 있는 운영자에게 매핑합니다. 이 예에서는 큐비트 사이에 ZZ 연산자를 사용하여 큐비트 간 거리가 멀어질 때의 동작을 살펴봅니다. 멀리 떨어진 큐비트 사이의 기대값이 점점 더 부정확해지면 존재하는 노이즈의 수준이 드러납니다.

from qiskit.quantum_info import SparsePauliOp
 
# ZZII...II, ZIZI...II, ... , ZIII...IZ
operator_strings = [
    "Z" + "I" * i + "Z" + "I" * (n - 2 - i) for i in range(n - 1)
]
print(operator_strings)
print(len(operator_strings))
 
operators = [SparsePauliOp(operator) for operator in operator_strings]

Output:

['ZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZI', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZ']
99

2단계. 양자 하드웨어에서 실행을 위한 문제 최적화

다음 코드는 백엔드의 ISA와 일치하도록 회로와 관측값을 변환합니다. 자격 증명을 이미 저장해 두어야 합니다.

from qiskit.transpiler import generate_preset_pass_manager
from qiskit_ibm_runtime import QiskitRuntimeService
 
service = QiskitRuntimeService()
 
backend = service.least_busy(
    simulator=False, operational=True, min_num_qubits=100
)
pm = generate_preset_pass_manager(optimization_level=1, backend=backend)
 
isa_circuit = pm.run(qc)
isa_operators_list = [op.apply_layout(isa_circuit.layout) for op in operators]

3단계. 하드웨어에서 실행

작업을 제출하고 동적 분리라는 오류를 줄이는 기술을 사용하여 오류 억제를 활성화합니다. 복원력 수준은 오류에 대비하여 얼마나 많은 복원력을 구축할지 지정합니다. 레벨이 높을수록 더 정확한 결과를 생성하지만 처리 시간이 길어지는 단점이 있습니다. 다음 코드에 설정된 옵션에 대한 자세한 설명은 Qiskit Runtime 에 대한 오류 완화 구성을 참조하세요

from qiskit_ibm_runtime import EstimatorOptions
from qiskit_ibm_runtime import EstimatorV2 as Estimator
 
options = EstimatorOptions()
options.resilience_level = 1
options.dynamical_decoupling.enable = True
options.dynamical_decoupling.sequence_type = "XY4"
 
# Create an Estimator object
estimator = Estimator(backend, options=options)

# Submit the circuit to Estimator
job = estimator.run([(isa_circuit, isa_operators_list)])
job_id = job.job_id()
print(job_id)

Output:

d2qkqt9olshc73bmb9g0

4단계. 후처리 결과

작업이 완료된 후 결과를 플롯하고 이상적인 시뮬레이션에서는 모두 $\langle Z_0 Z_i \rangle$ 이 1이어야 하지만 $i$ 이 증가함에 따라 $\langle Z_0 Z_i \rangle$ 이 감소하는 것을 확인합니다.

import matplotlib.pyplot as plt
from qiskit_ibm_runtime import QiskitRuntimeService
 
# data
data = list(range(1, len(operators) + 1))  # Distance between the Z operators
result = job.result()[0]
values = result.data.evs  # Expectation value at each Z operator.
values = [
    v / values[0] for v in values
]  # Normalize the expectation values to evaluate how they decay with distance.
 
# plotting graph
plt.plot(data, values, marker="o", label="100-qubit GHZ state")
plt.xlabel("Distance between qubits $i$")
plt.ylabel(r"$\langle Z_i Z_0 \rangle / \langle Z_1 Z_0 \rangle $")
plt.legend()
plt.show()

Output:

이전 플롯은 큐비트 사이의 거리가 증가함에 따라 노이즈의 존재로 인해 신호가 감쇠하는 것을 보여줍니다.

다음 단계

권장사항

이 튜토리얼 중 하나를 사용해 보세요:
- VQE를 사용한 하이젠베르크 체인의 기저 상태 에너지 추정
- QAOA를 사용하여 최적화 문제 해결
- 머신 러닝 작업을 위한 양자 커널 모델 훈련
자세한 설치 방법은 키스킷 설치 가이드에서 확인하세요.
로컬에 키스킷을 설치하지 않으려면 온라인 개발 환경에서 키스킷을 사용하는 옵션에 대해 읽어보세요
여러 개의 계정 자격 증명을 저장하거나 다른 계정 옵션을 지정하려면 로그인 자격 증명 저장하기 가이드의 자세한 안내를 참조하세요.

이 페이지가 도움이 되었습니까?

GitHub 에서 버그, 오타 또는 콘텐츠 요청을 신고하세요.