Esempi di Estimator

Versioni dei pacchetti

Il codice in questa pagina è stato sviluppato con i seguenti requisiti. Ti consigliamo di usare queste versioni o versioni più recenti.

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

Gli esempi in questa sezione illustrano alcuni modi comuni per usare Estimator. Prima di eseguire questi esempi, segui le istruzioni in Installa Qiskit.

nota

Tutti questi esempi usano le primitive IBM Quantum, ma in alternativa potresti usare le primitive di base.

Calcola e interpreta in modo efficiente i valori di aspettazione degli operatori quantistici richiesti da molti algoritmi con Estimator. Esplora gli utilizzi nella modellazione molecolare, nel machine learning e nei problemi di ottimizzazione complessi.

Esegui un singolo esperimento

Usa Estimator per determinare il valore di aspettazione di una singola coppia Circuit-osservabile.

# 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 SparsePauliOp, random_hermitian
from qiskit_ibm_runtime import QiskitRuntimeService

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

# Client-side primitive introduced in qiskit-ibm-runtime 0.50.0
from qiskit_ibm_runtime.executor_estimator import Estimator

n_qubits = 50

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)
observable = SparsePauliOp("Z" * 50)

pm = generate_preset_pass_manager(backend=backend, optimization_level=1)
isa_circuit = pm.run(circuit)
isa_observable = observable.apply_layout(isa_circuit.layout)

estimator = Estimator(mode=backend)
job = estimator.run([(isa_circuit, isa_observable)])
result = job.result()

print(f" > Expectation value: {result[0].data.evs}")
print(f" > Metadata: {result[0].metadata}")

> Expectation value: 0.012658227848101266
 > Metadata: {'shots': 4096, 'target_precision': 0.015625, 'circuit_metadata': {}, 'resilience': {}, 'num_randomizations': 32}

Esegui più esperimenti in un singolo job

Usa Estimator per determinare i valori di aspettazione di più coppie Circuit-osservabile.

import numpy as np
from qiskit.circuit.library import iqp
from qiskit.transpiler import generate_preset_pass_manager
from qiskit.quantum_info import SparsePauliOp, random_hermitian
from qiskit_ibm_runtime import QiskitRuntimeService

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

# Client-side primitive introduced in qiskit-ibm-runtime 0.50.0
from qiskit_ibm_runtime.executor_estimator import Estimator

n_qubits = 50

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)]

pubs = []
circuits = [iqp(mat) for mat in mats]
observables = [
    SparsePauliOp("X" * 50),
    SparsePauliOp("Y" * 50),
    SparsePauliOp("Z" * 50),
]

# Get ISA circuits
pm = generate_preset_pass_manager(optimization_level=1, backend=backend)

for qc, obs in zip(circuits, observables):
    isa_circuit = pm.run(qc)
    isa_obs = obs.apply_layout(isa_circuit.layout)
    pubs.append((isa_circuit, isa_obs))

estimator = Estimator(backend)
job = estimator.run(pubs)
job_result = job.result()

for idx in range(len(pubs)):
    pub_result = job_result[idx]
    print(f">>> Expectation values for PUB {idx}: {pub_result.data.evs}")
    print(f">>> Standard errors for PUB {idx}: {pub_result.data.stds}")

>>> Expectation values for PUB 0: -0.2650103519668737
>>> Standard errors for PUB 0: 0.49439861538856356
>>> Expectation values for PUB 1: -0.02099609375
>>> Standard errors for PUB 1: 0.013489459956524228
>>> Expectation values for PUB 2: 0.2788671023965142
>>> Standard errors for PUB 2: 0.4836236522960098

Esegui Circuit parametrizzati

Usa Estimator per eseguire tre esperimenti in un singolo job, sfruttando i valori dei parametri per aumentare la riusabilità del circuito.

import numpy as np

from qiskit.circuit import QuantumCircuit, Parameter
from qiskit.quantum_info import SparsePauliOp
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 Estimator

# Client-side primitive introduced in qiskit-ibm-runtime 0.50.0
from qiskit_ibm_runtime.executor_estimator import Estimator

service = QiskitRuntimeService()
backend = service.least_busy(operational=True, simulator=False)

# Step 1: Map classical inputs to a quantum problem
theta = Parameter("θ")

chsh_circuit = QuantumCircuit(2)
chsh_circuit.h(0)
chsh_circuit.cx(0, 1)
chsh_circuit.ry(theta, 0)

number_of_phases = 21
phases = np.linspace(0, 2 * np.pi, number_of_phases)
individual_phases = [[ph] for ph in phases]

ZZ = SparsePauliOp.from_list([("ZZ", 1)])
ZX = SparsePauliOp.from_list([("ZX", 1)])
XZ = SparsePauliOp.from_list([("XZ", 1)])
XX = SparsePauliOp.from_list([("XX", 1)])
ops = [ZZ, ZX, XZ, XX]

# Step 2: Optimize problem for quantum execution.

pm = generate_preset_pass_manager(backend=backend, optimization_level=1)
chsh_isa_circuit = pm.run(chsh_circuit)
isa_observables = [
    operator.apply_layout(chsh_isa_circuit.layout) for operator in ops
]

# Step 3: Execute using IBM Quantum primitives.

# Reshape observable array for broadcasting
reshaped_ops = np.fromiter(isa_observables, dtype=object)
reshaped_ops = reshaped_ops.reshape((4, 1))

estimator = Estimator(backend, options={"default_shots": int(1e4)})
job = estimator.run([(chsh_isa_circuit, reshaped_ops, individual_phases)])
# Get results for the first (and only) PUB
pub_result = job.result()[0]
print(f">>> Expectation values: {pub_result.data.evs}")
print(f">>> Standard errors: {pub_result.data.stds}")
print(f">>> Metadata: {pub_result.metadata}")

>>> Expectation values: [[ 0.9665404   0.90476418  0.77027221  0.53367788  0.27327396 -0.04118414
  -0.32496864 -0.5686415  -0.78657426 -0.91870673 -0.95667336 -0.91656172
  -0.78249875 -0.55877446 -0.2855005   0.0278851   0.31638861  0.58322755
   0.78829027  0.92428375  0.96267938]
 [ 0.01930507  0.31831912  0.5969556   0.80630833  0.92535625  0.96525339
   0.91312971  0.7689852   0.55062343  0.27992348 -0.01158304 -0.30287506
  -0.57679253 -0.80158932 -0.90562218 -0.96246488 -0.90669469 -0.77606373
  -0.55641496 -0.29193553  0.01630206]
 [-0.04719017 -0.35950326 -0.63599473 -0.84427497 -0.9669694  -1.00193302
  -0.93565229 -0.77992474 -0.53861139 -0.25782991  0.05898771  0.34834922
   0.62998871  0.83655294  0.95817487  1.00021701  0.93307828  0.77606373
   0.54547542  0.26662444 -0.06670973]
 [ 0.9969995   0.93543779  0.76426619  0.55963247  0.24967888 -0.06241972
  -0.35221024 -0.63620924 -0.83440793 -0.96568239 -1.00536503 -0.93415078
  -0.77949574 -0.56284998 -0.26855494  0.05362519  0.35070873  0.61797667
   0.84212996  0.97104491  0.99850101]]
>>> Standard errors: [[0.00518482 0.00616652 0.00723301 0.01064988 0.01139583 0.01174119
  0.01301757 0.01155567 0.00848267 0.00690879 0.00492396 0.00613768
  0.00678488 0.00840832 0.01404782 0.01184222 0.00982484 0.00854968
  0.00764619 0.00774419 0.00621175]
 [0.01590044 0.01095813 0.01205478 0.00872719 0.00609088 0.0043678
  0.00579195 0.00857024 0.01184119 0.01191681 0.01262258 0.01090978
  0.01346398 0.00940893 0.00709353 0.00454548 0.00795003 0.00900232
  0.00768466 0.01225787 0.01271092]
 [0.01265687 0.01230849 0.00961079 0.00725756 0.00469446 0.00444008
  0.00683132 0.00804195 0.01140408 0.01165563 0.01001761 0.01300941
  0.01014068 0.00822676 0.00511424 0.00465829 0.00659315 0.00633185
  0.00865837 0.0101667  0.01090357]
 [0.00399857 0.0064308  0.0071202  0.00974728 0.01066452 0.01082351
  0.01311009 0.01053503 0.00801145 0.00501261 0.00499458 0.00673144
  0.00871285 0.00998373 0.01241673 0.01345925 0.00835253 0.00686725
  0.00814337 0.00466632 0.00432618]]
>>> Metadata: {'shots': 10016, 'target_precision': 0.01, 'circuit_metadata': {}, 'resilience': {}, 'num_randomizations': 32}

Usa batch e opzioni avanzate

Esplora la modalità di esecuzione batch e le opzioni avanzate per ottimizzare le prestazioni del circuito sulle QPU.

import numpy as np
from qiskit.circuit.library import iqp
from qiskit.transpiler import generate_preset_pass_manager
from qiskit.quantum_info import SparsePauliOp, random_hermitian
from qiskit_ibm_runtime import QiskitRuntimeService, Batch

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

# Client-side primitive introduced in qiskit-ibm-runtime 0.50.0
from qiskit_ibm_runtime.executor_estimator import Estimator

n_qubits = 15

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)
mat = np.real(random_hermitian(n_qubits, seed=rng))
another_circuit = iqp(mat)
observable = SparsePauliOp("X" * n_qubits)
another_observable = SparsePauliOp("Y" * n_qubits)

pm = generate_preset_pass_manager(optimization_level=1, backend=backend)
isa_circuit = pm.run(circuit)
another_isa_circuit = pm.run(another_circuit)
isa_observable = observable.apply_layout(isa_circuit.layout)
another_isa_observable = another_observable.apply_layout(
    another_isa_circuit.layout
)

# The context manager automatically closes the batch.
with Batch(backend=backend) as batch:
    estimator = Estimator(mode=batch)

    estimator.options.resilience_level = 1

    job = estimator.run([(isa_circuit, isa_observable)])
    another_job = estimator.run(
        [(another_isa_circuit, another_isa_observable)]
    )
    result = job.result()
    another_result = another_job.result()

    # first job
    print(f" > Expectation value: {result[0].data.evs}")
    print(f" > Metadata: {result[0].metadata}")

    # second job
    print(f" > Another Expectation value: {another_result[0].data.evs}")
    print(f" > More Metadata: {another_result[0].metadata}")

> Expectation value: 0.026385707741639945
 > Metadata: {'shots': 4096, 'target_precision': 0.015625, 'circuit_metadata': {}, 'resilience': {}, 'num_randomizations': 32}
 > Another Expectation value: 0.0134052163776774
 > More Metadata: {'shots': 4096, 'target_precision': 0.015625, 'circuit_metadata': {}, 'resilience': {}, 'num_randomizations': 32}

Passi successivi

Raccomandazioni

• Specifica le opzioni avanzate di runtime.
• Esercitati con le primitive svolgendo la lezione sulle funzioni di costo in IBM Quantum® Learning.
• Scopri come eseguire la transpilazione in locale nella sezione Transpile.
• Prova la guida Confronta le impostazioni del Transpiler.
• Scopri i limiti dei job da rispettare quando invii un job a una QPU IBM®.