Using Qiskit to Run Quantum Workloads on IQM Hardware via QDMI-on-IQM

The iqm-qdmi Python package includes a wrapper for the IQM QDMI Device library that integrates it with Qiskit. This wrapper is implemented in the iqm.qdmi.qiskit submodule and is based on the open-source, MIT-licensed MQT Core library. To use the wrapper, make sure to install the iqm-qdmi package with the qiskit extra:

uv pip install iqm-qdmi[qiskit]

Then, the IQMBackend class can be imported from iqm.qdmi.qiskit and used as a drop-in replacement for any Qiskit backend.

from iqm.qdmi.qiskit import IQMBackend
from qiskit.circuit import QuantumCircuit
from qiskit.compiler import transpile

backend = IQMBackend(
  base_url="https://resonance.iqm.tech",
  qc_alias="emerald:mock",
)

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

transpiled_qc = transpile(qc, backend)
result = backend.run(transpiled_qc, shots=128).result()
print(result.get_counts())

{'00': 55, '11': 73}

If no explicit arguments are provided, the wrapper resolves IQM_BASE_URL, IQM_TOKEN, IQM_TOKENS_FILE, IQM_QC_ID, and IQM_QC_ALIAS from the environment. Alternatively, pass any of those values directly to IQMBackend(...).

Sampler and Estimator Primitives

IQMBackend provides small helpers (see sampler() and estimator()) for constructing BaseSamplerV2 and BaseEstimatorV2 primitives bound to the backend instance.

sampler_job = backend.sampler().run([(transpiled_qc,)], shots=128)
counts = sampler_job.result()[0].data["meas"].get_counts()
print(f"Counts: {counts}")

Counts: {'00': 71, '11': 57}

from qiskit.quantum_info import SparsePauliOp

transpiled_qc.remove_final_measurements(inplace=True)
observable = SparsePauliOp("Z" * backend.num_qubits)

estimator_job = backend.estimator().run([(transpiled_qc, observable)])
data = estimator_job.result()[0].data
print(f"Expectation values: {data['evs']}")
print(f"Standard deviations: {data['stds']}")

Expectation values: 0.041015625
Standard deviations: 0.031223703287380538

CLI Scripts

The package also exposes the iqm-sampler and iqm-estimator CLI scripts for executing serialized circuits directly from the shell. For more details on these utilities and their usage, see the Python Package Guide.