Esempi di Estimator
Versioni dei pacchetti
Il codice in questa pagina è stato sviluppato con i seguenti requisiti. Ti consiglio di usare queste versioni o versioni più recenti.
qiskit[all]~=2.4.0
qiskit-ibm-runtime~=0.46.1
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 di Qiskit Runtime, ma potresti usare le primitive di base invece.
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 — required packages for this notebook
!pip install -q numpy qiskit qiskit-ibm-runtime
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, EstimatorV2 as 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: -0.0564042303172738
> 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, EstimatorV2 as 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: 0.09218950064020487
>>> Standard errors for PUB 0: 0.2666311918779662
>>> Expectation values for PUB 1: -0.7159533073929961
>>> Standard errors for PUB 1: 0.5443960702392404
>>> Expectation values for PUB 2: -0.14271555996035679
>>> Standard errors for PUB 2: 0.2714876601210801
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, EstimatorV2 as 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 Qiskit 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: [[ 0.9821299 0.92848415 0.78219632 0.56555001 0.29732126 -0.02496591
-0.30928839 -0.5779298 -0.79292547 -0.92084995 -0.9806856 -0.93075378
-0.80014701 -0.57627916 -0.32496945 -0.00495192 0.29938456 0.56513735
0.80117866 0.92580187 0.98151091]
[-0.00330128 0.30949472 0.58123108 0.78549759 0.9357057 0.97903496
0.93240442 0.78879887 0.58267539 0.2948453 0.0041266 -0.29835291
-0.57339055 -0.78075201 -0.92477022 -0.97882863 -0.93075378 -0.79148116
-0.57958044 -0.30557445 0.00598356]
[-0.01031649 -0.34250749 -0.59257922 -0.80819387 -0.95159309 -0.99616033
-0.9336424 -0.78054568 -0.57112092 -0.30639977 0.00866585 0.30474913
0.57627916 0.81149515 0.95035511 0.99224006 0.9530374 0.78673557
0.57834246 0.30557445 -0.00866585]
[ 0.99616033 0.93446772 0.80344829 0.5841197 0.29401998 -0.01980766
-0.31300232 -0.59361087 -0.81170148 -0.94849814 -0.99327171 -0.93880064
-0.80860653 -0.58019943 -0.30186051 0.01856968 0.29009972 0.59835645
0.80613057 0.94437155 0.98976411]]
>>> Standard errors: [[0.00346988 0.00453617 0.00722056 0.00981693 0.01144016 0.01501324
0.01334599 0.01100181 0.00916772 0.00689316 0.00381375 0.00555949
0.00576968 0.01074419 0.01298665 0.01231428 0.0128399 0.00946472
0.00819982 0.00494361 0.00359142]
[0.01087106 0.01070164 0.00869617 0.00735853 0.00475886 0.00351362
0.00422178 0.00865889 0.00830071 0.01030088 0.01114086 0.01184411
0.00958307 0.00740947 0.00577496 0.00417023 0.00434772 0.00825295
0.00805684 0.01071724 0.01320466]
[0.01346985 0.01132597 0.01143045 0.00729025 0.00490636 0.00287136
0.0051666 0.00718324 0.00899331 0.00980723 0.00957352 0.01211162
0.00932736 0.00658862 0.00555066 0.00271584 0.00581507 0.00778402
0.00935326 0.01223799 0.01214173]
[0.00297333 0.00520897 0.00730712 0.01099862 0.01320699 0.01250301
0.0151248 0.00924768 0.00639241 0.00529221 0.00270411 0.00463968
0.00729108 0.00685512 0.00993793 0.0101938 0.01109962 0.01130657
0.00795711 0.00532976 0.00299901]]
>>> 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,
EstimatorV2 as 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: -0.03391665163268988
> Metadata: {'shots': 4096, 'target_precision': 0.015625, 'circuit_metadata': {}, 'resilience': {}, 'num_randomizations': 32}
> Another Expectation value: -0.011113040458412918
> 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 lavorando attraverso la lezione sulle funzioni di costo in IBM Quantum® Learning.
- Scopri come eseguire la trasposizione localmente nella sezione Transpile.
- Prova la guida Confronta le impostazioni del Transpiler.
- Leggi Migra alle primitive V2.
- Comprendi i limiti dei job quando invii un job a una QPU IBM®.