Algorithm tuning options
One of the advantages of the primitives is that they abstract away the circuit execution setup so that algorithm developers can focus on the pure algorithmic components. However, sometimes, to get the most out of an algorithm, you might want to tune certain primitive options. This section describes some of the common settings you might need.
This section focuses on Qiskit Runtime primitive Options (imported from qiskit_ibm_runtime). While
most of the primitives interface is common across implementations, most Options are not. Consult the
corresponding API references for information about the qiskit.primitives and qiskit_aer.primitives options.
1. Shots
For some algorithms, setting a specific number of shots is a core part of their routines. Previously, shots could be set during the call to backend.run(). For example, backend.run(shots=1024). Now, that setting is part of the execution
options ("second level option"). This can be done during the primitive setup:
from qiskit_ibm_runtime import Estimator, Options
options = Options()
options.execution.shots = 1024
estimator = Estimator(session=session, options=options)If you need to modify the number of shots set between iterations (primitive calls), you can set the
shots directly in the run() method. This overwrites the initial shots setting.
from qiskit_ibm_runtime import Estimator
estimator = Estimator(session=session)
estimator.run(circuits=circuits, observables=observables, shots=50)
# other logic
estimator.run(circuits=circuits, observables=observables, shots=100)For more information about the primitive options, refer to the Options class API reference.
2. Transpilation
By default, the Qiskit Runtime primitives perform circuit transpilation. There are several optimization levels you can choose from. These levels affect the transpilation strategy and might include additional error suppression mechanisms. Level 0 only involves basic transpilation. See the Optimization level table in the Error suppression topic for further details.
The optimization level option is a "first level option", and can be set as follows:
from qiskit_ibm_runtime import Estimator, Options
options = Options(optimization_level=2)
# or..
options = Options()
options.optimization_level = 2
estimator = Estimator(session=session, options=options)You might want to configure your transpilation strategy further, and for this, there are advanced transpilation options you can set up. These are "second level options", and can be set as follows:
from qiskit_ibm_runtime import Estimator, Options
options = Options()
options.transpilation.initial_layout = ...
options.transpilation.routing_method = ...
estimator = Estimator(session=session, options=options)For more information, and a complete list of advanced transpilation options, see the Advanced transpilation options table in the Error suppression topic.
Finally, you might want to specify settings that are not available through the primitives interface,
or use custom transpiler passes. In these cases, you can set skip_transpilation=True to submit
user-transpiled circuits. To learn how this is done, refer to the
Submitting user-transpiled circuits using primitives tutorial (opens in a new tab).
The skip_transpilation option is an advanced transpilation option, set as follows:
from qiskit_ibm_runtime import Estimator, Options
options = Options()
options.transpilation.skip_transpilation = True
estimator = Estimator(session=session, options=options)3. Error mitigation
You might want to leverage different error mitigation methods and see how these affect the performance of your
algorithm. These can also be set through the resilience_level option. The method selected for each level is
different for Sampler and Estimator. You can find more information in the
Configure error mitigation topic.
The configuration is similar to the other options:
from qiskit_ibm_runtime import Estimator, Options
options = Options(resilience_level = 2)
# or...
options = Options()
options.resilience_level = 2
estimator = Estimator(session=session, options=options)