Introduction to Qiskit Runtime sessions

What is a session?

A session is a Qiskit Runtime feature that lets you efficiently run multi-job iterative workloads on quantum computers. Using sessions helps avoid delays caused by queuing each job separately, which can be particularly useful for iterative tasks that require frequent communication between classical and quantum resources.

Advantages of using sessions

There are several benefits to using sessions:

• Efficiency: Multiple jobs from a single algorithm run can be run sequentially without interruptions.
• Flexibility: You can submit jobs, check results, and submit new jobs within an active session without needing to start a new one.

How sessions work

The basic workflow for sessions is as follows:

1. The first job in a session enters the normal queue.
2. When the first job starts running, the maximum timeout clock starts.
3. Subsequent jobs within the session are prioritized over others, reducing wait times.
4. The interactive timeout runs between the jobs in a session. Every session has an interactive timeout value (ITTL, or interactive time to live). If there are no session jobs queued within the ITTL window, the session is temporarily deactivated and normal job selection resumes. A deactivated session can be resumed for the next job^* if the session has not reached its maximum timeout value.
5. If the maximum timeout value is reached, the sessions end and any remaining queued jobs fail.

To find the maximum session timeout value for a session, follow the instructions in Determine session details.

For instructions to start a session, see Run a job in a session.

End a session

A session can end in the following circumstances:

• The maximum timeout is reached, resulting in the cancelation of all queued jobs.
• The session is manually canceled, resulting in the cancelation of all queued jobs.
• The session is manually closed. The session stops accepting new jobs but continues to run queued jobs with priority.

Usage patterns

Sessions run iteratively. This is useful for algorithms that require classical post-processing, where jobs submitted within the interactive time-out are processed immediately. If you want to submit jobs in a batch instead, see Run jobs in a batch.

Example: Run an iterative workload that uses the classical SciPy optimizer to minimize a cost function. In this model, SciPy uses the output of the cost function to calculate its next input.

def cost_func(params, ansatz, hamiltonian, estimator):
    # Return estimate of energy from estimator
 
    energy = estimator.run(ansatz, hamiltonian, parameter_values=params).result().values[0]
    return energy
 
x0 = 2 * np.pi * np.random.random(num_params)
 
session = Session(backend=backend)
 
estimator = Estimator(session=session, options={"shots": int(1e4)})
res = minimize(cost_func, x0, args=(ansatz, hamiltonian, estimator), method="cobyla")
 
# Close the session because we didn't use a context manager.
session.close()

Next steps