Encontrar cortes automaticamente

Etapa 1: Mapear

Crie um circuito e observáveis

# Added by doQumentation — required packages for this notebook
!pip install -q numpy qiskit qiskit-addon-cutting

import numpy as np
from qiskit.circuit.random import random_circuit
from qiskit.quantum_info import SparsePauliOp

circuit = random_circuit(7, 6, max_operands=2, seed=1242)
observable = SparsePauliOp(["ZIIIIII", "IIIZIII", "IIIIIIZ"])

circuit.draw("mpl", scale=0.8)

Etapa 2: Otimizar

Encontre locais de corte, dado um máximo de 4 qubits por subcircuito. Este circuito pode ser separado em dois fazendo um único wire cut e cortando uma CRZGate

from qiskit_addon_cutting.automated_cut_finding import (
    find_cuts,
    OptimizationParameters,
    DeviceConstraints,
)

# Specify settings for the cut-finding optimizer
optimization_settings = OptimizationParameters(seed=111)

# Specify the size of the QPUs available
device_constraints = DeviceConstraints(qubits_per_subcircuit=4)

cut_circuit, metadata = find_cuts(circuit, optimization_settings, device_constraints)
print(
    f'Found solution using {len(metadata["cuts"])} cuts with a sampling '
    f'overhead of {metadata["sampling_overhead"]}.\n'
    f'Lowest cost solution found: {metadata["minimum_reached"]}.'
)
for cut in metadata["cuts"]:
    print(f"{cut[0]} at circuit instruction index {cut[1]}")
cut_circuit.draw("mpl", scale=0.8, fold=-1)

Found solution using 2 cuts with a sampling overhead of 127.06026169907257.
Lowest cost solution found: True.
Wire Cut at circuit instruction index 19
Gate Cut at circuit instruction index 28

Adicione ancilas para os wire cuts e expanda os observáveis para incluir os qubits ancila

from qiskit_addon_cutting import cut_wires, expand_observables

qc_w_ancilla = cut_wires(cut_circuit)
observables_expanded = expand_observables(observable.paulis, circuit, qc_w_ancilla)
qc_w_ancilla.draw("mpl", scale=0.8, fold=-1)

Particione o circuito e os observáveis em subcircuitos e subobserváveis. Calcule o sampling overhead incorrido ao cortar essas portas e fios.

from qiskit_addon_cutting import partition_problem

partitioned_problem = partition_problem(
    circuit=qc_w_ancilla, observables=observables_expanded
)
subcircuits = partitioned_problem.subcircuits
subobservables = partitioned_problem.subobservables
print(
    f"Sampling overhead: {np.prod([basis.overhead for basis in partitioned_problem.bases])}"
)

Sampling overhead: 127.06026169907257

subobservables

{0: PauliList(['IIII', 'IZII', 'IIIZ']),
 1: PauliList(['ZIII', 'IIII', 'IIII'])}

subcircuits[0].draw("mpl", style="iqp", scale=0.8)

subcircuits[1].draw("mpl", style="iqp", scale=0.8)

Gere os experimentos para executar no backend.

from qiskit_addon_cutting import generate_cutting_experiments

subexperiments, coefficients = generate_cutting_experiments(
    circuits=subcircuits, observables=subobservables, num_samples=1_000
)
print(
    f"{len(subexperiments[0]) + len(subexperiments[1])} total subexperiments to run on backend."
)

96 total subexperiments to run on backend.

As etapas 3 e 4 de um padrão Qiskit podem então ser realizadas como nos tutoriais anteriores.