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‎docs/guides/multiverse-computing-singularity.ipynb‎

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" QiskitFunction(kipu-quantum/iskay-quantum-optimizer)]"
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{
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"ename": "ModuleNotFoundError",
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"evalue": "No module named 'sklearn'",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[3], line 8\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnumpy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;66;03m# Import the make_moons and the train_test_split functions from scikit-learn\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;66;03m# to create a synthetic dataset and split it into training and test datasets\u001b[39;00m\n\u001b[0;32m----> 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdatasets\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m make_moons\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodel_selection\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m train_test_split\n\u001b[1;32m 11\u001b[0m \u001b[38;5;66;03m# generate the synthetic dataset\u001b[39;00m\n",
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"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'sklearn'"
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"name": "stdout",
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"text": [
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"Features: [[ 0.84757037 -0.48831433]\n",
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" [ 0.98132552 0.19235443]\n",
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" [-0.71626723 0.6978261 ]\n",
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" [ 1.18957848 -0.48186557]\n",
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" [ 0.52118982 -0.37791846]\n",
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" [ 0.81115408 0.58483251]\n",
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" [ 0.48706462 0.87336593]\n",
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" [-0.81880144 0.57407682]\n",
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" [ 1.67335408 -0.23932015]\n",
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" [ 0.50181306 0.8649761 ]]\n",
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"Targets: [1 0 0 1 1 0 0 0 1 0]\n"
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]
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}
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],
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"# list available classifiers using the list action\n",
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"job = singularity.run(action=\"list\")\n",
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"\n",
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"# Print the ID so you can use it later, if necessary\n",
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"print(job.job_id)\n",
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"\n",
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"\n",
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"# you can also find your classifiers in the shared data directory with a *.pkl.tar extension\n",
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"version": "3.12.3"
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‎docs/guides/noise-learning.ipynb‎

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"---\n",
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"The error mitigation techniques [PEA](/docs/guides/error-mitigation-and-suppression-techniques#pea) and [PEC](/docs/guides/error-mitigation-and-suppression-techniques#pec) both utilize a noise learning component based on a [Pauli-Lindblad noise model](https://arxiv.org/abs/2201.09866), which is typically managed during execution after submitting one or more jobs through `qiskit-ibm-runtime` without any local access to the fitted noise model. However, as of `qiskit-ibm-runtime` v0.27.1, a [`NoiseLearner`](/docs/api/qiskit-ibm-runtime/noise-learner) and associated [`NoiseLearnerOptions`](/docs/api/qiskit-ibm-runtime/options-noise-learner-options) class have been created to obtain the results of these noise learning experiments. These results can then be stored locally as a `NoiseLearnerResult` and used as input in later experiments. This page provides an overview of its usage and the associated options available.\n",
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"The resulting `NoiseLearnerResult.data` is a list of [`LayerError`](../api/qiskit-ibm-runtime/utils-noise-learner-result-layer-error) objects containing the [noise model](https://arxiv.org/abs/2201.09866) for each individual entangling layer that belongs to the target circuit(s). Each `LayerError` stores the layer information, in the form of a circuit and a set of qubit labels, alongside the [`PauliLindbladError`](../api/qiskit-ibm-runtime/utils-noise-learner-result-pauli-lindblad-error) for the noise model that was learned for the given layer."
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"The `LayerError.error` attribute of the noise learning result contains the generators and error rates of the fitted Pauli Lindblad model, which has the form\n",
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"id": "c819a7f6-7f42-4b08-9a33-282afac9b51f",
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"After the noise model is passed into the `EstimatorV2` object, it can be used to run workloads and perform error mitigation as normal."
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"## NoiseLearnerV3\n",
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"The job result is a list of `NoiseLearnerV3Result` objects, one for each input-boxed set of instructions. `NoiseLearnerV3Result` has a `to_pauli_lindblad_map()` method that returns a [`PauliLindbladMap`](/docs/api/qiskit/qiskit.quantum_info.PauliLindbladMap) object, which has methods to extract the generators, error rates, and more."
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"The result of the `NoiseLearnerV3` job must be converted to a dictionary before being passed to Executor. This dictionary's keys are the `InjectNoise.ref` attributes and the values are the corresponding noise maps. This mapping tells Executor which noise models to inject where.\n",
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"## Next steps\n",
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‎docs/guides/q-ctrl-optimization-solver.ipynb‎

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