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 Sep 7, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Latest release

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
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

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

iqm_benchmarks-2.60.3.tar.gz (210.5 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

iqm_benchmarks-2.60.3-py3-none-any.whl (231.9 kB view details)

Uploaded Python 3

File details

Details for the file iqm_benchmarks-2.60.3.tar.gz.

File metadata

  • Download URL: iqm_benchmarks-2.60.3.tar.gz
  • Upload date:
  • Size: 210.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.14.3

File hashes

Hashes for iqm_benchmarks-2.60.3.tar.gz
Algorithm Hash digest
SHA256 0cbe68b678f2905122c527ab6ecc8c5374e4237e6ffd2b72abb81223bdb40471
MD5 c106be308b3c06c7ed17e6fcf64f55d7
BLAKE2b-256 0e09619e5e4169db02e8e7c339cb5433e638cdfb811b42a47c7a2e6e18adcd8f

See more details on using hashes here.

File details

Details for the file iqm_benchmarks-2.60.3-py3-none-any.whl.

File metadata

File hashes

Hashes for iqm_benchmarks-2.60.3-py3-none-any.whl
Algorithm Hash digest
SHA256 389c6a675497f5bfb077c6be3d6baf4678f16aebd0847330044e3203075d2700
MD5 179d9b7aa50242b81231fc35251f2463
BLAKE2b-256 8d1760dcfb7d7f59e639e7d565d021001392f08b5d69d370537de5abe5d6e925

See more details on using hashes here.

Release history Release notifications | RSS feed

This release

2.60.3 This release

2 files

2.60.2

2 files

2.60.1

2 files

2.60.0

2 files

2.59.4

2 files

2.59.3

2 files

2.59.2

2 files

2.59.1

2 files

2.59.0

2 files

2.58

2 files

2.57

2 files

2.56

2 files

2.55

2 files

2.54

2 files

2.53

3 files

2.52

2 files

2.51

2 files

2.50

2 files

2.49

2 files

2.48

2 files

2.47

2 files

2.46

2 files

2.45

2 files

2.44

2 files

2.43

2 files

2.42

2 files

2.41

2 files

2.40

2 files

2.39

2 files

2.38

2 files

2.37

2 files

2.36

2 files

2.35

2 files

2.34

2 files

2.33

2 files

2.32

2 files

2.31

2 files

2.30

2 files

2.29

2 files

2.28

2 files

2.27

2 files

2.26

2 files

2.25

2 files

2.24

2 files

2.23

2 files

2.22

2 files

2.21

2 files

2.20

2 files

2.19

2 files

2.18

2 files

2.17

2 files

2.16

2 files

2.14

2 files

2.13

2 files

2.12

2 files

2.11

2 files

2.10

2 files

2.9

2 files

2.8

2 files

2.7

2 files

2.6

2 files

2.5

2 files

2.4

2 files

2.3

2 files

2.2

2 files

2.1

2 files

2.0

2 files

1.12

2 files

1.11

2 files

1.10

2 files

1.9

2 files

1.8

2 files

1.7

2 files

1.6

2 files

1.5

2 files

1.4

2 files

1.3

2 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page