Contoh Sampler

Versi pakej

Kod di halaman ini dibangunkan menggunakan keperluan berikut. Kami syorkan menggunakan versi ini atau yang lebih baru.

qiskit[all]~=2.5.2
qiskit-ibm-runtime~=0.47.0

Jana keseluruhan taburan quasi-kebarangkalian dengan mitigasi ralat yang disampel dari output Circuit kuantum. Manfaatkan keupayaan Sampler untuk algoritma carian dan pengelasan seperti Grover dan QVSM.

Jalankan satu eksperimen

Gunakan Sampler untuk mengembalikan hasil pengukuran sebagai bitstring atau kiraan bagi satu Circuit.

# Added by doQumentation — installs the packages this notebook needs if they are missing
import importlib.util

_needed = {"numpy": "numpy", "qiskit": "qiskit", "qiskit_ibm_runtime": "qiskit-ibm-runtime"}
_missing = [pip for module, pip in _needed.items()
            if importlib.util.find_spec(module) is None]
# One at a time, so a package that fails to install does not block the others
for _pip in _missing:
    %pip install -q {_pip}
if not _missing:
    print("\u2713 All required packages are installed")

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

# Legacy server-side primitive before qiskit-ibm-runtime 0.50.0
# from qiskit_ibm_runtime import Sampler

# Client-side primitive introduced in qiskit-ibm-runtime 0.50.0
from qiskit_ibm_runtime.executor_sampler import 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: ['0111010010000101000101110000000101010000100111110000110011101100001100101111100100010110000110101001100010100001111010000110000', '0010001000101011111011110111001100101010110111001101100111100000011010010000100000011101010011101010000100101001101100000110001', '0010100110101011001001010111010110111011000101110001001011111010011010001000010010010001110010000001100000100110010000110010001', '1111000001010000010111000010100111001110101000100101000001110110001110010100010100010110001000001000100001101100100001101010100', '1100111010001011001011001010111100011100110010110011110010100001011101100110111000010000011110101010110101100001011000000000000', '0010111110001001110000001110001110010001111110100111100001001011000010000111000010011000100100000001001100001110000001100000110', '0110010101111101101111001101011100111000101110101101010100101010010000010000011000000100101110001010010110101001110001010000110', '0000001111111000001101101010111011010001111101101001111110100101100001110010000111011000000010101000100000000001101110000000001', '1011001100100101111000001000100100001011001001100001001010111011001000001010100111010001001110010101110000100001000100101111001', '1111100010100111000011010101000110011011110111011000000010101000100011000001000100000101000110001000000001001011101101110011000']

Jalankan berbilang eksperimen dalam satu kerja

Gunakan Sampler untuk mengembalikan hasil pengukuran sebagai bitstring atau kiraan bagi berbilang Circuit dalam satu kerja.

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

# Legacy server-side primitive before qiskit-ibm-runtime 0.50.0
# from qiskit_ibm_runtime import Sampler

# Client-side primitive introduced in qiskit-ibm-runtime 0.50.0
from qiskit_ibm_runtime.executor_sampler import 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]}"
    )

> 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']

Jalankan Circuit berparameter

Jalankan beberapa eksperimen dalam satu kerja, memanfaatkan nilai parameter untuk meningkatkan kebolehgunaan semula Circuit.

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

# Legacy server-side primitive before qiskit-ibm-runtime 0.50.0
# from qiskit_ibm_runtime import Sampler

# Client-side primitive introduced in qiskit-ibm-runtime 0.50.0
from qiskit_ibm_runtime.executor_sampler import 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]}"
)

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

Gunakan kumpulan dan pilihan lanjutan

Terokai mod pelaksanaan kumpulan dan pilihan lanjutan untuk mengoptimumkan prestasi Circuit pada QPU.

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, QiskitRuntimeService

# Legacy server-side primitive before qiskit-ibm-runtime 0.50.0
# from qiskit_ibm_runtime import Sampler

# Client-side primitive introduced in qiskit-ibm-runtime 0.50.0
from qiskit_ibm_runtime.executor_sampler import Sampler

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]}"
)

> 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],
)

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

Langkah seterusnya

Cadangan

• Tentukan pilihan runtime lanjutan.
• Amalkan penggunaan primitif melalui pelajaran Fungsi kos dalam IBM Quantum Learning.
• Ketahui cara mentranspil secara tempatan dalam bahagian Transpil.
• Cuba panduan Bandingkan tetapan Transpiler.
• Fahami Had kerja apabila menghantar kerja kepada QPU IBM®.