サンプル例

このページのコードは、以下の要件に基づいて開発されました。これらのバージョン以降のご利用をお勧めします。
```
qiskit[all]~=2.5.1
qiskit-ibm-runtime~=0.47.0
```

量子回路の出力からサンプリングされた、エラーの影響を軽減した準確率分布全体を生成する。 GroverやQVSMなどの検索・分類アルゴリズムにおいて、Samplerの機能を活用してください。

1つの実験を実行する

Sampler を使用して、単一の回路の測定結果をビット列またはカウントとして返します。

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: ['1111010010110011001010101100010100001010110000100110111000000000100011100000001101110110001010000100000000010000000011000110101', '1001001111111001011011011011001010100101010000001101000010101101010000011100000000100100010000001000010000001010001001010101111', '0100001101111001110000000001000101101010001000010110111100011000100000010101101110001000010001111110001000100010011110000001100', '1000101001100101010000100001000101101010000011001110101111100010111011010110001010101010011011000001100000000010100100010100111', '1100011110101010000000011000100000100001110101011011100011011000111111110010000101000000000101011100001000100101000000000100001', '0000001100000000101100000000110100101011110100101101100110000000100110001110100000010010100000011101011001000000001011000100101', '1000001100110111001110100011101000111111101100110011100000000000000100001000100101100110000000100101000101001001110000001110000', '1100001000101000101100010011010101001010110010101000110111010100000100000011110000110011010110011010110010000000000000000000101', '0111010011101111010010000011010010001000000000010100000001001010001111100000100101000101000111110010101010100000101000100101011', '0100001000101010110010100111110100101001011111000011111010100110011000100001100000111101100101000000010010010000011110001011000']

1つのジョブで複数の実験を実行する

Sampler を使用して、1つのジョブ内で複数の回路の測定結果をビット列またはカウントとして返す。

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: ['0101000101101010001010110001000101011010001000011001101011100011010001000000011001010110011001100000010001000001000100001111011', '0001010011100000101110011011110001110001000101000101011100101101010001000110101000001000101010000001000001000101101100000101000', '0100100110010110000000101011101100011000000101111110111111010001010000000010000010101110100101100111000101100010100111000010100', '1001011100111110000100011111110001011001100100001010000101010000111010000001100111110001101101001010110100000001010000010110000', '0001101101111010100001110101000011100001100001011101110100000110100001001101011110111011001011000101010110000010000111000001100']
 > First five results for pub 1: ['1111011001010000011101010001110000011000100000000001101010100000001100001001011010000100110100110111000001011000010010000000110', '0100111011011010011111101001110101101000100000000011111101011000100010000001110110010011000111010000100010101001011001001101110', '0110001000111101001000101000101000010010100010010000011011110001111010000000011010100000110000010000111101000010001001000100100', '1110110111110000010111101000111000100011110110011001100011000101000111110001001010000110100000011001100011101100000000000101010', '1000010011110101101101111100011000100101001011110010000101011100010111101100111001101111111111010100011010110100011000100100001']
 > First five results for pub 2: ['0100010111001111010001100100111010110000001000110000111010111001000011101110000110110010010000001000100100000010101000000001100', '0000110110001001100000001000101000001101010100011010111000101011011110000101011010000110000000000100000001010110010010000000001', '0001000111100100110100101100011011000010100001000100100000010001101110000000010101100111100100111101010111111001100101100010010', '0011110010110101011001111100000101000010001111000101100000001100110011111000000100010010101110111001011100000000001010000001001', '1110000001000100001010011100110100011110101010100100111111000110010111000001001100111000101001001100011011010010111000101000011']

パラメータ化された回路を実行する

1つのジョブ内で複数の実験を実行し、パラメータ値を活用して回路の再利用性を高めます。

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: ['1001010001111101000000100001100110000001110001011100011001110011101111001110110100110101011001100100011001110001110011011100011', '1000101001000011110100010010001111101110000001111100001010100000100000100110101111110011000000111001010100110001011011101001111', '0110111100011101011000100011000011000010110110000100101100010101111001101011111110011111100000100011111001101101001111011110101', '0110111011101011011111000100000011110011010000010000100110000011101000111100011100100110111000110100111000101011111100010100111', '0000001110100110101011011110110011111100011111001011010101111100000010111110010100001110001001110000001011110011001001000001111']

バッチ処理と詳細オプションの使用

QPU上で回路のパフォーマンスを最適化するために、バッチ実行モードと詳細設定について詳しく見ていきましょう。

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: ['1001001101100100001000001111101111001011010010010110110001110000000101010010001101001111000010110010101011001110110111001000100', '0100000111100101000010001110100001000011000011010000100001011000001001010111110100010000111101011100000100001110010110110001010', '1100011001000001101101000000000111001011110101110100001001000001001001100000101010010000000000110011000000011010011011100001111', '0011111111110001010010101111110111000010100001010000011101100010011011110001001000001100101000010100101010100010001001010001010', '1001111101110101010101110110011101111010011101000101110100011011110100000100100100110001001110101000000100101001001111000001010']

# 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: ['1111111110000001000111010010010101010010111001110111001000100000010011101110101101001010001010000000000100011000010001000010000', '1110011100110100100100111001000101010011110001010110100100001110010010011100000000000100000010001001010100011110010000001011100', '1111101001010011110011011010000111000010001101100101000100000110000011001110001101100100100100100010011100001000000000100111010', '1100010101000011101000110100000101001000110110010100000000001000010110100110000111010101010010001101010010100000100111010110000', '1010100100100110011100010010100000101101101101000111000010101110010111010100001111000001100010100011110000000011101000101001100']

次のステップ

推奨事項

詳細な実行時オプションを指定します。
IBM Quantum Learning の「 Cost関数」のレッスンに取り組んで、プリミティブ型の扱い方を練習しましょう。
「トランスパイル」のセクションで、ローカルでのトランスパイル方法について学びましょう。
「トランスパイラ設定の比較」ガイドをお試しください。
IBM® のQPUにジョブを送信する際は、ジョブの制限事項を理解しておく必要があります。

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