How to perform a cutting workflow with multiple observables¶

Create a circuit to cut¶

[1]:

from qiskit.circuit.library import EfficientSU2

qc = EfficientSU2(4, entanglement="linear", reps=2).decompose()
qc.assign_parameters([0.4] * len(qc.parameters), inplace=True)

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

[1]:

../_images/how-tos_how_to_provide_multiple_observables_2_0.png

Specify the observables of interest¶

[2]:

from qiskit.quantum_info import Pauli, SparsePauliOp

observables = [
    Pauli("-XZII"),
    SparsePauliOp(["ZZII", "IZZI", "-IIZZ", "XIXI", "ZIZZ", "IXIX"]),
]

Gather unique observable terms¶

[3]:

from qiskit_addon_cutting.utils.observable_terms import gather_unique_observable_terms

unique_observable_terms = gather_unique_observable_terms(observables)

Perform cutting workflow¶

[4]:

from qiskit_addon_cutting import partition_problem

partitioned_problem = partition_problem(
    circuit=qc, partition_labels="AABB", observables=unique_observable_terms
)
subcircuits = partitioned_problem.subcircuits
subobservables = partitioned_problem.subobservables

[5]:

import numpy as np
from qiskit_addon_cutting import generate_cutting_experiments

subexperiments, coefficients = generate_cutting_experiments(
    circuits=subcircuits, observables=subobservables, num_samples=np.inf
)

[6]:

from qiskit_ibm_runtime.fake_provider import FakeManilaV2

backend = FakeManilaV2()

[7]:

from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager

# Transpile the subexperiments to ISA circuits
pass_manager = generate_preset_pass_manager(optimization_level=1, backend=backend)
isa_subexperiments = {
    label: pass_manager.run(partition_subexpts)
    for label, partition_subexpts in subexperiments.items()
}

[8]:

from qiskit_ibm_runtime import SamplerV2, Batch

# Submit each partition's subexperiments as a single batch
with Batch(backend=backend) as batch:
    sampler = SamplerV2(mode=batch)
    jobs = {
        label: sampler.run(subsystem_subexpts, shots=2**12)
        for label, subsystem_subexpts in isa_subexperiments.items()
    }

[9]:

# Retrieve results
results = {label: job.result() for label, job in jobs.items()}

Reconstruct the expectation value of each unique term¶

[10]:

from qiskit_addon_cutting import reconstruct_expectation_values

# Get expectation values for each observable term
reconstructed_term_expvals = reconstruct_expectation_values(
    results,
    coefficients,
    subobservables,  # or unique_observable_terms if the circuit did not separate
)

Reconstruct the expectation value of the original operators¶

[11]:

from qiskit_addon_cutting.utils.observable_terms import (
    reconstruct_observable_expvals_from_terms,
)

reconstructed_expvals = reconstruct_observable_expvals_from_terms(
    observables, dict(zip(unique_observable_terms, reconstructed_term_expvals))
)

Compare the reconstructed expectation values with the exact expectation value from the original circuit and observables¶

[12]:

from qiskit_aer.primitives import EstimatorV2

estimator = EstimatorV2()
exact_expvals = estimator.run([(qc, observables)]).result()[0].data.evs
print(
    f"Reconstructed expectation values: {np.real(np.round(reconstructed_expvals, 8))}"
)
print(f"Exact expectation values: {np.round(exact_expvals, 8)}")
print(
    f"Error in estimation: {np.real(np.round(reconstructed_expvals-exact_expvals, 8))}"
)
print(
    f"Relative error in estimation: {np.real(np.round((reconstructed_expvals-exact_expvals) / exact_expvals, 8))}"
)

Reconstructed expectation values: [-0.20063001  0.48694563]
Exact expectation values: [-0.17994235  0.56254612]
Error in estimation: [-0.02068766 -0.07560049]
Relative error in estimation: [ 0.11496824 -0.13438986]