Work with experiment artifacts#

Problem#

You want to view, add, remove, and save artifacts associated with your ExperimentData instance.

Solution#

Artifacts are used to store auxiliary data for an experiment that don’t fit neatly in the AnalysisResult model. Any data that can be serialized, such as fit data, can be added as ArtifactData artifacts to ExperimentData.

For example, after an experiment that uses CurveAnalysis is run, its ExperimentData object is automatically populated with fit_summary and curve_data artifacts. The fit_summary artifact has one or more CurveFitResult objects that contain parameters from the fit. The curve_data artifact has a ScatterTable object that contains raw and fitted data in a pandas DataFrame.

Viewing artifacts#

Here we run a parallel experiment consisting of two T1 experiments in parallel and then view the output artifacts as a list of ArtifactData objects accessed by ExperimentData.artifacts():

from qiskit_ibm_runtime.fake_provider import FakePerth
from qiskit_aer import AerSimulator
from qiskit_experiments.library import T1
from qiskit_experiments.framework import ParallelExperiment
import numpy as np

backend = AerSimulator.from_backend(FakePerth())
exp1 = T1(physical_qubits=[0], delays=np.arange(1e-6, 6e-4, 5e-5))
exp2 = T1(physical_qubits=[1], delays=np.arange(1e-6, 6e-4, 5e-5))
data = ParallelExperiment([exp1, exp2], flatten_results=True).run(backend).block_for_results()
data.artifacts()

[ArtifactData(name=curve_data, dtype=ScatterTable, uid=c68629c4-3b7a-4cba-b538-0dcba022308e, experiment=T1, device_components=[<Qubit(Q0)>]),
 ArtifactData(name=fit_summary, dtype=CurveFitResult, uid=b9ba1a71-7df3-487e-990f-a217b7466dac, experiment=T1, device_components=[<Qubit(Q0)>]),
 ArtifactData(name=curve_data, dtype=ScatterTable, uid=9da85a97-3f91-4b7b-81e6-99695dc4aa11, experiment=T1, device_components=[<Qubit(Q1)>]),
 ArtifactData(name=fit_summary, dtype=CurveFitResult, uid=c71f217a-4170-4fe5-867b-27b621fba6da, experiment=T1, device_components=[<Qubit(Q1)>])]

Artifacts can be accessed using either the artifact ID, which has to be unique in each ExperimentData object, or the artifact name, which does not have to be unique and will return all artifacts with the same name:

print("Number of curve_data artifacts:", len(data.artifacts("curve_data")))
# retrieve by name and index
curve_data_id = data.artifacts("curve_data")[0].artifact_id
# retrieve by ID
scatter_table = data.artifacts(curve_data_id).data
print("The first curve_data artifact:\n")
scatter_table.dataframe

Number of curve_data artifacts: 2
The first curve_data artifact:
xval yval yerr series_name series_id category shots analysis
0 0.000001 0.976098 0.004769 exp_decay 0 raw 1024 T1Analysis
1 0.000051 0.767317 0.013192 exp_decay 0 raw 1024 T1Analysis
2 0.000101 0.613171 0.015205 exp_decay 0 raw 1024 T1Analysis
3 0.000151 0.467805 0.015577 exp_decay 0 raw 1024 T1Analysis
4 0.000201 0.405366 0.015328 exp_decay 0 raw 1024 T1Analysis
... ... ... ... ... ... ... ... ...
119 0.000529 0.092408 0.00543 exp_decay 0 fitted <NA> T1Analysis
120 0.000534 0.09049 0.005523 exp_decay 0 fitted <NA> T1Analysis
121 0.00054 0.088624 0.005616 exp_decay 0 fitted <NA> T1Analysis
122 0.000545 0.086808 0.00571 exp_decay 0 fitted <NA> T1Analysis
123 0.000551 0.085042 0.005804 exp_decay 0 fitted <NA> T1Analysis

124 rows × 8 columns

In composite experiments, artifacts behave like analysis results and figures in that if flatten_results isn’t True, they are accessible in the artifacts() method of each child_data(). The artifacts in a large composite experiment with flatten_results=True can be distinguished from each other using the experiment and device_components attributes.

One useful pattern is to load raw or fitted data from curve_data for further data manipulation. You can work with the dataframe using standard pandas dataframe methods or the built-in ScatterTable methods:

import matplotlib.pyplot as plt

exp_type = data.artifacts(curve_data_id).experiment
component = data.artifacts(curve_data_id).device_components[0]

raw_data = scatter_table.filter(category="raw")
fitted_data = scatter_table.filter(category="fitted")

# visualize the data
plt.figure()
plt.errorbar(raw_data.x, raw_data.y, yerr=raw_data.y_err, capsize=5, label="raw data")
plt.errorbar(fitted_data.x, fitted_data.y, yerr=fitted_data.y_err, capsize=5, label="fitted data")
plt.title(f"{exp_type} experiment on {component}")
plt.xlabel('x')
plt.ylabel('y')
plt.legend()
plt.show()
../_images/artifacts_2_0.png

Adding artifacts#

You can add arbitrary data as an artifact as long as it’s serializable with ExperimentEncoder, which extends Python’s default JSON serialization with support for other data types commonly used with Qiskit Experiments.

from qiskit_experiments.framework import ArtifactData

new_artifact = ArtifactData(name="experiment_notes", data={"content": "Testing some new ideas."})
data.add_artifacts(new_artifact)
data.artifacts("experiment_notes")

ArtifactData(name=experiment_notes, dtype=dict, uid=ee2f7541-8c46-4dfd-a8f2-2a984dce23dd, experiment=None, device_components=[])

print(data.artifacts("experiment_notes").data)

{'content': 'Testing some new ideas.'}

Saving and loading artifacts#

Note

This feature is only for those who have access to the cloud service. You can check whether you do by logging into the IBM Quantum interface and seeing if you can see the database.

Artifacts are saved and loaded to and from the cloud service along with the rest of the ExperimentData object. Artifacts are stored as .zip files in the cloud service grouped by the artifact name. For example, the composite experiment above will generate two artifact files, fit_summary.zip and curve_data.zip. Each of these zipfiles will contain serialized artifact data in JSON format named by their unique artifact ID:

fit_summary.zip
|- b9ba1a71-7df3-487e-990f-a217b7466dac.json
|- c71f217a-4170-4fe5-867b-27b621fba6da.json
curve_data.zip
|- c68629c4-3b7a-4cba-b538-0dcba022308e.json
|- 9da85a97-3f91-4b7b-81e6-99695dc4aa11.json
experiment_notes.zip
|- ee2f7541-8c46-4dfd-a8f2-2a984dce23dd.json

Note that for performance reasons, the auto save feature does not apply to artifacts. You must still call ExperimentData.save() once the experiment analysis has completed to upload artifacts to the cloud service.

Note also though individual artifacts can be deleted, currently artifact files cannot be removed from the cloud service. Instead, you can delete all artifacts of that name using delete_artifact() and then call ExperimentData.save(). This will save an empty file to the service, and the loaded experiment data will not contain these artifacts.

See Also#