Primitives¶

Qiskit’s V2 primitives are the standard interface for sampling and expectation-value workloads. mimiq-qiskit ships native implementations that talk to MIMIQ directly, rather than relying on Qiskit’s generic BackendSamplerV2 / BackendEstimatorV2 wrappers.

The native primitives differ from the generic wrappers in two ways:

MimiqEstimatorV2 computes expectation values exactly using MIMIQ’s expectation-value engine. The generic estimator estimates them by sampling in rotated measurement bases; MIMIQ evaluates \(\langle \psi | H | \psi \rangle\) directly, so there is no shot noise and the reported standard deviation is zero.
Both primitives submit every circuit in a pub (one per parameter binding) as a single MIMIQ job, instead of one round trip per circuit.

Sampling¶

from qiskit import QuantumCircuit
from mimiqlink import MimiqConnection
from mimiq_qiskit import MimiqBackend, MimiqSamplerV2

conn = MimiqConnection(); conn.connect()
sampler = MimiqSamplerV2(MimiqBackend(conn))

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

result = sampler.run([qc], shots=1000).result()
# Per-register bit arrays; ``measure_all`` writes the "meas" register.
print(result[0].data.meas.get_counts())

Estimating expectation values¶

from qiskit import QuantumCircuit
from qiskit.quantum_info import SparsePauliOp
from mimiq_qiskit import MimiqBackend, MimiqEstimatorV2

estimator = MimiqEstimatorV2(MimiqBackend(conn))

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

observable = SparsePauliOp(["ZZ", "XX"], [1.0, 0.5])
result = estimator.run([(qc, observable)]).result()
print(result[0].data.evs)   # exact expectation value

Parameterised circuits broadcast the usual way: pass an array of bindings as the third pub element, and the result’s evs / bit arrays take the broadcast shape.