Release Notes¶

v0.26.1-25¶

New Features¶

A new utility get_runtime_service() is now available to simplify the instantiation of a QiskitRuntimeService within a function. It automatically pulls credentials from environment variables, reducing boilerplate code and improving usability for function developers.

Example usage:

from qiskit_serverless import get_runtime_service
service = get_runtime_service()
backend = service.backend("ibm_fez")

This is equivalent to:

import os
from qiskit_serverless import get_arguments, save_result
from qiskit_ibm_runtime import QiskitRuntimeService
service = QiskitRuntimeService(
  channel=os.environ["QISKIT_IBM_CHANNEL"],
  instance=os.environ["QISKIT_IBM_INSTANCE"],
  token=os.environ["QISKIT_IBM_TOKEN"],
)
backend = service.backend("ibm_fez")

This function also enables automatic tracking of runtime job and session IDs created during function execution. It’s important to note that tracking will only be possible if the function runtime jobs are submitted through a service instantiated with this function.

On top of the default credentials, function developers can customize the call to get_runtime_service() with custom input parameters, including token, instance, channel and url. For example:

from qiskit_serverless import get_runtime_service
service = get_runtime_service(channel="ibm_quantum_platform", 
                              token="staging_token", 
                              instance="staging_crn", 
                              url="staging_url")
backend = service.backend("staging_backend")

When using get_runtime_service() inside a serverless function, the resulting job object now supports two new methods: job.runtime_jobs() and job.runtime_sessions(). These methods return lists of job/session IDs that can be used to fetch job objects from a QiskitRuntimeService or access IQP dashboards. job.runtime_jobs() accepts an optional runtime_session parameter that allows to filter the returned jobs by associated session id. For example:

job = function.run(...)

runtime_ids = job.runtime_jobs()
# out = ["job_id_1", "job_id_2", "job_id_3"...]
runtime_sessions =  job.runtime_sessions()
# out = ["session_id_1", "session_id_2"]

# a specific session id can be passed to the runtime_jobs() method to filter by session:

session_id = job.runtime_sessions()[0]
# out = "session_id_1"
session_job_ids =  job.runtime_jobs(session_id)
# in this example, only job ids 1 and 3 correspond to session_id_1:
# out = ["job_id_1", "job_id_3"]

Upgrade Notes¶

Added a new AccessPolicy for User model to be able to allow or not the access to the service by the is_active parameter.

Enhanced job.cancel() behavior to use the newly introduced features. job.cancel() now attempts to instantiate a QiskitRuntimeService using the credentials from the ServerlessClient, allowing automatic canceling of associated runtime jobs. This works when the credentials used in the function match those in the client, which happens by default when using get_runtime_service() with no additional inputs.

To support local testing or non-standard runtime URLs (e.g., staging environments), where the ServerlessClient don’t match those in the QiskitRuntimeService used to submit the jobs, job.cancel() accepts a service parameter:
```
service = QiskitRuntimeService(
  channel="ibm_quantum_platform",
  token="MY_TOKEN",
  instance="MY_CRN",
  url="my.staging.url.com"
)
job.cancel(service)
```

The Scheduler will start to manage the size of the logs generated by the Qiskit Functions. The environment variable FUNCTIONS_LOGS_SIZE_LIMIT will be in charge of the maximum size that the system will allow to store.

Enhanced client.jobs() and function.jobs() behavior with new filters to improve the management of the different jobs. Now you can filter by status and by created_after to retrieve those specific jobs. For example:

# Filtering by status it will retrieve all the jobs with that status
client.jobs(status="SUCCEEDED")

# The same it will apply after a specific date
time_filter = datetime.now(timezone.utc)
client.jobs(created_after=time_filter)

# And all these new filters can be combined with the Qiskit Function filter
# to be able to return running jobs from the specific Qiskit Function
function.jobs(status="RUNNING")