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IBM Quantum Platform

Beispiele für Kostenvoranschläge

  • Der Code auf dieser Seite wurde unter Berücksichtigung der folgenden Anforderungen entwickelt. Wir empfehlen, diese Versionen oder neuere zu verwenden.

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

Die Beispiele in diesem Abschnitt veranschaulichen einige gängige Anwendungsmöglichkeiten von Estimator. Bevor Sie diese Beispiele ausführen, befolgen Sie bitte die Anweisungen unter „Qiskit installieren “.

Note

In diesen Beispielen werden durchweg die „ IBM Quantum “-Primitive verwendet, Sie könnten jedoch stattdessen auch die Basisprimitive verwenden.

Berechnen und interpretieren Sie mit Estimator effizient die Erwartungswerte der für viele Algorithmen erforderlichen Quantenoperatoren. Entdecken Sie Anwendungsmöglichkeiten in den Bereichen Molekülmodellierung, maschinelles Lernen und komplexe Optimierungsprobleme.

Ein einzelnes Experiment durchführen

Verwenden Sie den Schätzer, um den Erwartungswert eines einzelnen Paares aus Schaltung und Messgröße zu bestimmen.

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}")

Output:

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

Führen Sie mehrere Experimente in einem einzigen Auftrag durch

Verwenden Sie den Schätzer, um die Erwartungswerte mehrerer Paare aus Schaltkreisen und Messgrößen zu bestimmen.

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}")

Output:

>>> 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

Parametrisierte Schaltkreise ausführen

Führen Sie mit Estimator drei Experimente in einem einzigen Auftrag durch und nutzen Sie dabei Parameterwerte, um die Wiederverwendbarkeit der Schaltungen zu erhöhen.

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}")

Output:

>>> 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}

Stapelverarbeitung und erweiterte Optionen verwenden

Entdecken Sie den B atch-Ausführungsmodus und die erweiterten Optionen zur Optimierung der Schaltungsleistung auf QPUs.

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}")

Output:

 > 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}

Nächste Schritte

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