Qiskit Integration

MENOS integrates with Qiskit through two classes in menos.qiskit:

Class	Qiskit interface	Purpose
MENOSBackend	BackendV2	Run circuits and get counts or a statevector
MENOSEstimator	BaseEstimatorV2	Compute expectation values

MENOSJob (a JobV1) is the object run returns. There is no provider class and no SamplerV2 primitive.

Running circuits

from qiskit import QuantumCircuit

from menos.qiskit import MENOSBackend

backend = MENOSBackend()
qc = QuantumCircuit(3, 3)
qc.h(0)
qc.cx(0, 1)
qc.cx(1, 2)
qc.measure([0, 1, 2], [0, 1, 2])

job = backend.run(qc, shots=500, seed_simulator=3)
print(job.status(), job.result().get_counts())
assert job.result().success

Options are shots (default 1024), seed_simulator (default: a fresh random seed) and want_statevector (default False). Anything else raises AttributeError. See MENOSBackend.

Transpiling

MENOSBackend advertises a Target built from exactly the gates it can execute, so Qiskit's transpiler knows how to lower a circuit for it:

from qiskit import QuantumCircuit, transpile
from qiskit.circuit.library import QFTGate

from menos.qiskit import MENOSBackend

backend = MENOSBackend()
qc = QuantumCircuit(3)
qc.append(QFTGate(3), [0, 1, 2])
qc.measure_all()

lowered = transpile(qc, backend, seed_transpiler=0)
print(sorted(lowered.count_ops()))
counts = backend.run(lowered, shots=100, seed_simulator=1).result().get_counts()
assert sum(counts.values()) == 100

The backend's target has no fixed qubit count (num_qubits=None), matching Qiskit's own BasicSimulator, because the real limit is memory, not a device size.

What the converter accepts

Circuit content	Behaviour
A gate in the supported table	Run natively
Any other gate with a matrix on 1 to 5 qubits	Lowered to a dense unitary and run
A gate on more than 5 qubits that is not native	UnsupportedFeatureError ("try transpile(qc, backend) first")
barrier, id, delay	Dropped; they do not change the result
measure	Supported, but must be terminal for its qubit
reset, classical control (if_test), mid-circuit measurement	UnsupportedFeatureError
Unbound Parameters	InvalidCircuitError; call assign_parameters first

Global phase is preserved: a circuit's global_phase is part of the statevector MENOS returns.

Estimator

from qiskit import QuantumCircuit
from qiskit.quantum_info import SparsePauliOp

from menos.qiskit import MENOSEstimator

qc = QuantumCircuit(2)
qc.h(0)
qc.cx(0, 1)

estimator = MENOSEstimator()
pub_result = estimator.run([(qc, [SparsePauliOp("ZZ"), SparsePauliOp("XX")])]).result()[0]
print(pub_result.data.evs)

Results are exact, so stds is always zero and any requested precision is recorded in the metadata but never used to add noise. See EstimatorV2.

What the Qiskit layer does not expose

The Qiskit classes cover the common path and deliberately nothing more. These are reachable only through the native API:

• time and memory budgets (time_budget_seconds, memory_budget_bytes)
• the plan report, explain()
• arbitrary observables other than through MENOSEstimator

Backend selection (CPU or CUDA) is automatic and is not exposed anywhere in Python.