Introduction to Qiskit Runtime execution modes

There are several ways to run workloads, depending on your needs.

Job mode: A single primitive request of the estimator or the sampler made without a context manager. Circuits and inputs are packaged as primitive unified blocs (PUBs) and submitted as an execution task on the quantum computer. To run in job mode, specify mode=backend when instantiating a primitive. See Primitives examples for examples.

Session mode: A dedicated window for running a multi-job workload. This allows users to experiment with variational algorithms in a more predictable way and even run multiple experiments simultaneously taking advantage of parallelism in the stack. Use sessions for iterative workloads or experiments that require dedicated access. To run in session mode, specify mode=session when instantiating a primitive or run the job in a session context manager. See Run jobs in a session for examples.

Batch mode: A multi-job manager for efficiently running an experiment that is comprised of bundle of independent jobs. Use batch mode to submit multiple primitive jobs simultaneously. To run in batch mode, specify mode=batch when instantiating a primitive or run the job in a batch context manager. See Run jobs in a batch for examples.

The differences are summarized in the following table:

Mode	Usage	Benefit
Job mode	Quantum computation only.	Easiest to use when running a small experiment. Might run sooner than batch mode.
Batch mode	Quantum computation only.	The entire batch of jobs is scheduled together and there is no additional queuing time for each. Jobs in a batch are usually run close together.
Session mode	Both classical and quantum computation.	Dedicated and exclusive access to the QPU during the session active window, and no other users’ or QPU jobs can run. This is particularly useful for workloads that don’t have all inputs ready at the outset.

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Best practices

To ensure the most efficient use of the execution modes, the following practices are recommended:

• Always close your session, either by using a context manager or by specifying session.close().

• There is a fixed overhead associated with running a job. In general, if each of your jobs uses less than one minute of QPU time, consider combining several into one larger job (this applies to all execution modes). "QPU time" refers to time spent by the QPU complex to process your job.

  A job's QPU time is listed in the Usage column on the IBM Quantum Platform Jobs(opens in a new tab) page, or you can query it by using qiskit-ibm-runtime with this command job.metrics()["usage"]["quantum_seconds"]

• If each of your jobs consumes more than one minute of QPU time, or if combining jobs is not practical, you can still run multiple jobs in parallel. Every job goes through both classical and quantum processing. While a QPU can process only one job at a time, up to five classical jobs can be processed in parallel. You can take advantage of this by submitting multiple jobs in batch or session execution mode.

The above are general guidelines, and you should tune your workload to find the optimal ratio, especially when using sessions. For example, if you are using a session to get exclusive access to a backend, consider breaking up large jobs into smaller ones and running them in parallel. This might be more cost effective because it can reduce wall clock time.

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Sessions versus batch usage

Usage is a measurement of the amount of time the QPU is locked for your workload.

• Session usage is the time from when the first job starts until the session goes inactive, is closed, or when its last job completes, whichever happens last.
• Batch usage is the amount of time all jobs spend on the QPU.
• Single job usage is the quantum time they use in processing.

To find the usage time for your session or batch, in qiskit-ibm-runtime 0.23 or later, run session.details()["usage_time"] or batch.details()["usage_time"].

If you are calling the REST API directly, the usage time is the elapsed_time value returned by the GET /sessions/{id} endpoint, for both session and batch workloads.