IQM Benchmarks

IQM Benchmarks is a suite of Quantum Characterization, Verification, and Validation (QCVV) tools for quantum computing. It is designed to be a comprehensive tool for benchmarking quantum hardware. The suite is designed to be modular, allowing users to easily add new benchmarks and customize existing ones. The suite is designed to be easy to use, with a simple API that allows users to run benchmarks with a single command.

Below is a list of the benchmarks currently available in the suite:

The project is split into different benchmarks, all inheriting from the Benchmark parent class. Each individual benchmark takes as an argument their own BenchmarkConfigurationBase class.

Characterize Physical Hardware

The IQM Benchmarks suite is designed to be used with real quantum hardware. To use the suite, you will need to have access to a quantum computer. The suite is designed to work with both IQM Resonance (IQM's quantum cloud service) and on-prem devices, but can be easily adapted to work with other quantum computing platforms.

To use the suite with IQM Resonance, you will need to set up an account and obtain an API token. You can then set the IQM_TOKEN environment variable to your API token. The suite will automatically use this token to authenticate with IQM Resonance.

import os
os.environ["IQM_TOKEN"] = "your_token"

Using a Jupyter notebook or Python script

You can easily set up one or more benchmarks by defining a configuration for them. For example, for Randomized, Interleaved and Mirror Benchmarking, or Quantum Volume:

from iqm.benchmarks.randomized_benchmarking.interleaved_rb.interleaved_rb \
    import InterleavedRBConfiguration
from iqm.benchmarks.randomized_benchmarking.mirror_rb.mirror_rb \
    import MirrorRBConfiguration
from iqm.benchmarks.quantum_volume.quantum_volume \
    import QuantumVolumeConfiguration

EXAMPLE_IRB = InterleavedRBConfiguration(
    qubits_array=[[3,4],[8,9]],
    sequence_lengths=[2**(m+1)-1 for m in range(7)],
    num_circuit_samples=30,
    shots=2**10,
    parallel_execution=True,
    interleaved_gate = "iSwapGate",
    interleaved_gate_params = None,
    simultaneous_fit = ["amplitude", "offset"],
)

EXAMPLE_MRB = MirrorRBConfiguration(
    qubits_array=[[0,1],
                  [0,1,3,4],
                  [0,1,3,4,8,9],
                  [0,1,3,4,8,9,13,14],
                  [0,1,3,4,8,9,13,14,17,18]],
    depths_array=[[2**m for m in range(9)],
                  [2**m for m in range(8)],
                  [2**m for m in range(7)],
                  [2**m for m in range(6)],
                  [2**m for m in range(5)]],
    num_circuit_samples=10,
    num_pauli_samples=5,
    shots=2**8,
    two_qubit_gate_ensemble={"CZGate": 0.7, "iSwapGate": 0.3},
    density_2q_gates=0.25,
)

EXAMPLE_QV = QuantumVolumeConfiguration(
    num_circuits=800,
    shots=2**8,
    num_sigmas=2,
    choose_qubits_routine="custom",
    custom_qubits_array=[[0,1,2,3], [0,1,3,4]],
    max_circuits_per_batch=100,
    rem=True,
    mit_shots=1_000,
)

In order to execute them, you must specify a backend:

  • First you need to specify the quantum computer name, i.e. "garnet"
  • Then you need to specify the base URL of the quantum computer.

Also, you need to reference the benchmark configuration you want to run:

from iqm.benchmarks.randomized_benchmarking.mirror_rb.mirror_rb import *
from iqm.qiskit_iqm import IQMProvider

quantum_computer = "" # the quantum computer name. i.e. "emerald", "garnet", "sirius"
iqm_server_url = "https://resonance.iqm.tech/" # base url

provider = IQMProvider(iqm_server_url, quantum_computer=quantum_computer)
backend = provider.get_backend()

EXAMPLE_EXPERIMENT = MirrorRandomizedBenchmarking(backend, EXAMPLE_MRB)
EXAMPLE_EXPERIMENT.run()

Full examples on how to run benchmarks and analyze the results can be found in the docs/examples folder.

Installation

Note: uv is highly recommended for practical Python environment and package management. The supported Python versions are

=3.11, <3.15.

uv pip install iqm-benchmarks

Optional extras

The following optional extras are available:

  • examples: Example Jupyter notebooks and related dependencies

All extras are inter-compatible and each adds more dependencies on top of the base install. To install all of them:

uv pip install "iqm-benchmarks[examples]"

Reproducible installations

For reproducible installations, pinned dependency extras are available:

  • pin-iqm: pins inter-IQM package dependencies to exact co-released versions.
  • pin-all: like pin-iqm, but additionally pins all transitive third-party dependencies.

These can be combined with other extras. The following combined extras are available:

  • examples-pin-iqm
  • examples-pin-all

For example:

uv pip install "iqm-benchmarks[examples-pin-all]"

All extras are inter-compatible, so you can combine multiple extras with pinning as needed.

Note: This package was tested at release time for clean installation with uv 0.11.20 and pip 25.1.1. Other versions are likely to work but are not verified per release.

Project links

Data verified by PyPI on Aug 31, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

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  • Released:
Newer release available (2.60.3)

1 maintainer

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Avatar for IQM from gravatar.com IQM

Credits

Author: IQM Finland Oy

License

Apache License

Requires

Python <3.15, >=3.11

Provides Extra

examples examples-pin-all examples-pin-iqm pin-all pin-iqm

Classifiers

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