Hello world

패키지 버전

이 페이지의 코드는 다음 요구 사항을 사용하여 개발되었습니다. 이 버전 또는 그 이상의 버전을 사용하는 것을 권장합니다.

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

이 예제는 두 부분으로 구성되어 있습니다. 먼저 간단한 양자 프로그램을 작성하고 양자 처리 장치(QPU)에서 실행합니다. 실제 양자 연구에는 훨씬 더 견고한 프로그램이 필요하기 때문에, 두 번째 섹션( 대규모 큐비트 확장 )에서는 간단한 프로그램을 실용 수준으로 확장할 것입니다.

설치 및 인증

Qiskit을 아직 설치하지 않았다면, 빠른 시작 가이드에서 설치 방법을 확인하세요.
- 설치하여 양자 Qiskit Runtime 하드웨어에서 작업을 실행하세요:
```
pip install qiskit-ibm-runtime
```
- 로컬에서 Jupyter 노트북을 실행할 환경을 설정하세요:
```
pip install jupyter
```
무료 Open Plan 을 통해 양자 하드웨어 접근을 위한 인증을 설정하세요.

(계정 가입 초대 이메일을 받은 경우, 초대받은 사용자를 위한 단 계를 따르십시오.)
- 로그인하거나 계정을 생성하려면 로 IBM Quantum Platform 이동하세요.
- 대시보드 에서 API 키( API 토큰이라고도 함)를 생성한 후 안전한 위치에 복사하세요.
- 인스턴스 페이지로 이동하여 사용하려는 인스턴스를 찾으세요. 해당 CRN 위에 마우스를 올려놓고 클릭하여 복사하세요.
- 이 코드로 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 서비스에 Qiskit Runtime 인증할 때마다 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 않으십니까?

공용 컴퓨터나 기타 보안이 취약한 환경에서 접속하는 경우, 인증 정보를 안전하게 보호하기 위해 수동 인증 절차를 따르십시오.

간단한 양자 프로그램을 작성하고 실행하기

Qiskit 패턴을 사용하여 양자 프로그램을 작성하는 네 단계는 다음과 같습니다:

문제를 양자 본연의 형식으로 매핑하십시오.
회로와 연산자를 최적화하십시오.
양자 원시 함수를 사용하여 실행하십시오.
결과를 분석하십시오.

1단계. 문제를 양자 원시 형식으로 매핑한다

양자 프로그램에서 양자 회로는 양자 명령을 표현하는 기본 형식이며, 연산자는 측정 대상 관측량을 나타낸다. 회로를 생성할 때는 일반적으로 새 QuantumCircuit 객체를 생성한 다음 순차적으로 명령어를 추가합니다.

다음 코드 셀은 두 큐비트가 완전히 얽힌 상태인 벨 상태를 생성하는 회로를 만듭니다.

참고: 비트 순서

LSB 0 비트 번호 체계를 사용하며 Qiskit SDK, 여기서 0번째 자리는 $n^{th}$ 0 또는 $2^n$ 1의 $1 \ll n$ 값을 가집니다. 자세한 내용은 해당 Qiskit SDK 항목의 비트 순서 지정 섹션을 참조하십시오.

from qiskit import QuantumCircuit
from qiskit.quantum_info import SparsePauliOp
from qiskit.transpiler import generate_preset_pass_manager
from qiskit_ibm_runtime import QiskitRuntimeService
from qiskit_ibm_runtime import EstimatorOptions
from qiskit_ibm_runtime import EstimatorV2 as Estimator
from matplotlib import pyplot as plt
# Uncomment the next line if you want to use a simulator:
# from qiskit_ibm_runtime.fake_provider import FakeBelemV2
 
 
# 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:

사용 가능한 모든 작업은 문서에서 QuantumCircuit 확인하십시오.

양자 회로를 생성할 때는 실행 후 어떤 유형의 데이터를 반환받기를 원하는지도 고려해야 합니다. Qiskit은 데이터를 반환하는 두 가지 방법을 제공합니다: 측정하기로 선택한 큐비트 집합에 대한 확률 분포를 얻을 수 있거나, 관측량의 기대값을 얻을 수 있습니다. 회로를 측정하기 위해 다음 두 가지 방법 중 하나로 작업 부하를 준비하십시오( Qiskit primitives 자세한 내용은 3단계에서 설명됨).

이 예제는 양자 상태를 변화시키는 작용이나 과정을 나타내는 수학적 객체인 연산자를 사용하여 지정된 qiskit.quantum_info 서브모듈을 통해 기대값을 측정합니다. 다음 코드 셀은 여섯 개의 2큐비트 파울리 연산자를 생성합니다: IZ, IX, ZI, XI, ZZ, 및 XX.

# 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 연산자와 유사한 것은 텐서곱 의 약어로서, 큐비트 1에 대해 Z를 측정하고 큐비트 0에 대해 Z를 함께 $Z\otimes Z$ 측정하여 큐비트 1과 큐비트 0 사이의 상관관계에 대한 정보를 얻는 것을 의미한다. 이와 같은 기대값은 일반적으로 다음과 같이 $\langle Z_1 Z_0 \rangle$ 표기됩니다.

만약 상태가 얽혀 있다면, 의 측정은 의 $\langle Z_1 Z_0 \rangle$ 측정과 $\langle I_1 \otimes Z_0 \rangle \langle Z_1 \otimes I_0 \rangle$ 달라야 한다. 위에서 설명한 회로로 생성된 특정 얽힘 상태의 경우, 의 측정은 1이어야 하며 $\langle Z_1 Z_0 \rangle$ 의 측정은 $\langle I_1 \otimes Z_0 \rangle \langle Z_1 \otimes I_0 \rangle$ 0이어야 한다.

2단계. 회로와 연산자를 최적화하십시오

장치에서 회로를 실행할 때, 회로가 포함하는 명령어 집합을 최적화하고 회로의 전체 깊이(대략 명령어 수)를 최소화하는 것이 중요하다. 이는 오류와 잡음의 영향을 줄여 가능한 최상의 결과를 얻을 수 있도록 보장합니다. 또한 회로의 명령어는 백엔드 장치의 명령어 집합 아키텍처(ISA) 를 준수해야 하며, 장치의 기본 게이트와 큐비트 연결성을 고려해야 합니다.

다음 코드는 작업을 제출할 실제 장치를 인스턴스화하고, 해당 백엔드의 ISA에 맞도록 회로와 관측 가능 객체를 변환합니다. 이미 자격 증명을 저장해 두어야 합니다

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 원시 연산을 사용하여 실행

양자 컴퓨터는 무작위 결과를 생성할 수 있으므로, 일반적으로 회로를 여러 번 실행하여 출력값의 샘플을 수집합니다. 관측 가능한 값은 Estimator 클래스를 사용하여 추정할 수 있습니다. Estimator 두 가지 기본 연산 중 하나이며, 다른 하나는 양자 컴퓨터로부터 데이터를 얻는 데 사용할 수 있는 입니다 Sampler. 이러한 객체들은 원시 통합 블록( PUB ) 을 사용하여 회로, 관측 가능량 및 매개변수(해당하는 경우)의 선택을 실행하는 run() 메서드를 보유합니다

# 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: d5k96q4jt3vs73ds5tgg

작업이 제출된 후에는 현재 파이썬 인스턴스 내에서 작업이 완료될 때까지 기다리거나, 나중에 데이터를 가져오기 위해 job_id 를 사용할 수 있습니다. (자세한 내용은 작업 검색 섹션을 참조하십시오.)

작업이 완료된 후, 작업의 result() 속성을 통해 그 출력을 확인하십시오.

# 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 예제를 실행하십시오

실제 장치에서 양자 프로그램을 실행할 때, 작업 부하가 실행되기 전에 대기열에서 대기해야 합니다. 시간을 절약하려면, 대신 다음 코드를 사용하여 로컬 Qiskit Runtime 테스트 모드에서 fake_provider 이 작은 워크로드를 실행할 수 있습니다. 이것은 소규모 회로에서만 가능합니다. 다음 섹션에서 확장할 때는 실제 기기를 사용해야 합니다.

 
# Use the following code instead if you want to run on a simulator:
 
from qiskit_ibm_runtime.fake_provider import FakeBelemV2
backend = FakeBelemV2()
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. 결과를 분석하십시오

분석 단계에서는 일반적으로 측정 오차 완화나 제로 노이즈 외삽법(ZNE) 등을 사용하여 결과를 후처리할 수 있습니다. 이러한 결과를 추가 분석을 위해 다른 워크플로에 입력하거나 주요 값과 데이터의 플롯을 준비할 수 있습니다. 일반적으로 이 단계는 귀하의 문제에 특화된 것입니다. 이 예시를 위해, 회로에서 측정된 각 기대값을 그래프로 표시하십시오.

Estimator에 지정한 관측값의 기대값과 표준편차는 작업 결과의 PubResult.data.evs 및 PubResult.data.stds 속성을 통해 접근할 수 있습니다. 샘플러로부터 결과를 얻으려면 함수를 PubResult.data.meas.get_counts() 사용하십시오. 이 함수는 키로 비트열 형태의 측정값을, 해당 값으로 dict 개수를 반환하는 를 반환합니다. 자세한 내용은 샘플러 시작하기를 참조하십시오

# Plot the result
 
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:

양자비트 0과 1에 대해, X와 Z의 독립적 기대값은 모두 0인 반면, 상관관계(XX 와 ZZ)는 1임을 유의하십시오. 이는 양자 얽힘의 특징이다.

대규모 큐비트 수로 확장

양자 컴퓨팅 분야에서 실용적 규모의 작업은 해당 분야의 진전을 이루는 데 핵심적이다. 이러한 작업은 훨씬 더 큰 규모로 계산을 수행해야 하며, 100개 이상의 큐비트와 1000개 이상의 게이트를 사용할 수 있는 회로로 작업해야 합니다. 이 예시는 100큐비트 GHZ 상태를 생성하고 분석함으로써 IBM® QPU에서 유틸리티 규모의 작업을 수행하는 방법을 보여줍니다. Qiskit 패턴 워크플로를 사용하며, 각 큐비트에 대한 $\langle Z_0 Z_i \rangle$ 기대값을 측정함으로써 종료됩니다.

1단계. 문제를 매핑하다

100-큐비트 $n$ GHZ 상태를 준비하고 측정할 관측량을 수집하기 위해, 100-큐비트 GHZ 상태(본질적으로 확장된 벨 상태)를 QuantumCircuit 준비하는 함수를 반환하는 함수를 작성하십시오.

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 연산자로 매핑합니다. 이 예제는 큐비트 사이의 ZZ 연산자를 사용하여 큐비트가 멀어질수록 나타나는 행동을 살펴봅니다. 먼 큐비트들 사이에서 점점 부정확해지는(손상된) 기대값은 존재하는 잡음의 수준을 드러낼 것이다.

# 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 실행하기 위한 문제 최적화

다음 코드는 회로와 관측 가능 객체를 백엔드의 ISA에 맞게 변환합니다. 이미 자격 증명을 저장해 두어야 합니다

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

동적 분리(dynamic decoupling) 라는 오류 감소 기법을 사용하여 작업을 제출하고 오류 억제를 활성화하십시오 복원력 수준은 오류에 대비하여 구축할 복원력의 정도를 지정합니다. 높은 수준일수록 더 정확한 결과를 생성하지만, 처리 시간이 더 오래 걸리는 단점이 있습니다. 다음 코드에서 설정된 옵션에 대한 자세한 설명은 . Qiskit Runtime 에 대한 오류 완화 구성 항목을 참조하십시오

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:

d5k9mmqvcahs73a1ni3g

4단계. 후처리 결과

작업이 완료된 후 결과를 그래프로 표시하면, $i$ 가 증가함에 따라 가 감소하는 $\langle Z_0 Z_i \rangle$ 것을 확인할 수 있습니다. 이상적인 시뮬레이션에서는 모든 $\langle Z_0 Z_i \rangle$ 가 1이어야 함에도 불구하고 말입니다.

# 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 그래프는 큐비트 간 거리가 증가함에 따라 잡음의 존재로 인해 신호가 감쇠함을 보여줍니다.

다음 단계

권장사항

다음 튜토리얼 중 하나를 시도해 보세요:
- VQE를 이용한 하이젠베르크 사슬의 기저 상태 에너지 추정
- QAOA를 사용하여 최적화 문제 해결
- 머신러닝 작업을 위한 양자 커널 모델 훈련
자세한 설치 지침은 Qiskit 설치 가이드에서 확인하세요.
로컬에 Qiskit을 설치하지 않으려면, 온라인 개발 환경에서 Qiskit을 사용하는 방법에 대해 알아보세요
여러 계정 자격 증명을 저장하거나 다른 계정 옵션을 지정하려면 '로그인 자격 증명 저장하기' 가이드의 자세한 지침을 참조하십시오.

이 페이지가 도움이 되었습니까?

GitHub 에서 버그, 오타 또는 콘텐츠 요청을 신고하세요.