i+`SrSSKJr SSKJr SSKJrJrJr SSK J r J r J r J r Jr SSKrSSKJr SSKJr SS KJr SS KJr S S KJr S S KJr S SKJr S SKJr S SK J!r! S SK"J#r# S SK$J%r%J&r& S SK'J(r( S SK)J*r* S SK+J,r, S SK-J.r. S SK/J0r0 S SK1J2r2 \Rf"\45r5"SS5r6g)zNoise learner program.) annotations)deepcopy)asdictfieldsreplace)AnyDictIterableOptionalUnionN)QuantumCircuit) BackendV2)EstimatorPubLike) EstimatorPub)_get_mode_service_backend)DEFAULT_DECODERS) RuntimeJobV2) IBMBackend)EstimatorOptions)NoiseLearnerOptions)remove_dict_unset_valuesremove_empty_dict)validate_isa_circuits) is_simulator)QiskitRuntimeLocalService)QiskitRuntimeService)Session)Batchc\rSrSrSrS S Sjjr\SSj5rSSjr\ SSj5r SSjr SSS jjr \ SS j5rS rg) NoiseLearner0u Class for executing noise learning experiments. The noise learner class allows characterizing the noise processes affecting the gates in one or more circuits of interest, based on the Pauli-Lindblad noise model described in [1]. The :meth:`~run` method allows running a noise learner job for a list of circuits. After the job is submitted, the gates are collected into independent layers, and subsequently the resulting layers are characterized individually. The way in which the gates are collected into layers depends on the twirling ``strategy`` specified in the given ``options`` (see :class:`.NoiseLearnerOptions` for more details). Note that all strategies obey barriers. For example, if you have three ISA entangling layers of interest, consider putting them into one circuit separated by barriers acting on the qubits you wish to twirl, and select ``strategy="active-circuit"``. The following snippet shows an example where the noise learner is used to characterized the layers of two GHZ circuits. .. code-block:: python from qiskit.circuit import QuantumCircuit from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager from qiskit_ibm_runtime import QiskitRuntimeService from qiskit_ibm_runtime.noise_learner import NoiseLearner from qiskit_ibm_runtime.options import NoiseLearnerOptions service = QiskitRuntimeService() backend = service.least_busy(operational=True, simulator=False) # a circuit returning a two-qubit GHZ state ghz = QuantumCircuit(2) ghz.h(0) ghz.cx(0, 1) # another circuit returning a two-qubit GHZ state another_ghz = QuantumCircuit(3) another_ghz.h(0) another_ghz.cx(0, 1) another_ghz.cx(1, 2) another_ghz.cx(0, 1) pm = generate_preset_pass_manager(backend=backend, optimization_level=1) circuits = pm.run([ghz, another_ghz]) # set the options options = NoiseLearnerOptions() options.layer_pair_depths = [0, 1, 10] # run the noise learner job learner = NoiseLearner(backend, options) job = learner.run(circuits) Args: mode: The execution mode used to make the primitive query. It can be: * A :class:`Backend` if you are using job mode. * A :class:`Session` if you are using session execution mode. * A :class:`Batch` if you are using batch execution mode. Refer to the `Qiskit Runtime documentation `__ for more information about the execution modes. options: :class:`NoiseLearnerOptions`. Alternatively, :class:`EstimatorOptions` can be provided for convenience, in which case the estimator options get reformatted into noise learner options and all the irrelevant fields are ignored. References: 1. E. van den Berg, Z. Minev, A. Kandala, K. Temme, *Probabilistic error cancellation with sparse Pauli–Lindblad models on noisy quantum processors*, Nature Physics volume 19, pages 1116–1121 (2023). `arXiv:2201.09866 [quant-ph] `_ Nc[U5uUlUlUl[ UR[ 5(a [ S5eURU5 g)Nz7``NoiseLearner`` not currently supported in local mode.)r_mode_service_backend isinstancer ValueError _set_options)selfmodeoptionss h/home/james-whalen/.local/lib/python3.13/site-packages/qiskit_ibm_runtime/noise_learner/noise_learner.py__init__NoiseLearner.__init__|sH 4MT3R0 DM4= dmm%> ? ?VW W '"cUR$)z"The options in this noise learner.)_optionsr*s r-r,NoiseLearner.optionss}}r0c [SU55(dAUVs/sHn[R"U5PM nnUVs/sHoDRPM nn[ UR 5nSUR U50n[R"U5nSU0nURU5 Sn UR(aUHn [URSS5(a;[UR5(d![U /URR5 [UR[ 5(dM{URR#U 5 M [URSS5n [$R'SU5 UR((aMUR(R+UR-5UU[.R0"UR-55U S9$UR(aMURUS 'S U;a8[UR[ 5(aURR2US '[UR4[65(aMUR4R+UR-5UU[.R0"UR-55U S 9$UR4R+UR-5UUU S 9$s snfs snf) aSubmit a request to the noise learner program. This function breaks the given list of circuits into a list of unique layers, following the strategy set by the ``twirling_strategy`` field specified in the ``options`` (see :class:`NoiseLearnerOptions` for more details) and sorting them based on the number of times they occur in the various circuits. Then, it runs the noise learning experiment for as many layers as specified by the ``max_layers_to_learn`` field in the ``options``, prioritizing layers that occur more frequently. Args: circuits: An iterable of circuits to run the noise learner program for. Alternatively, estimator pub-like (primitive unified bloc) objects can be specified, such as tuples ``(circuit, observables)`` or ``(circuit, observables, parameter_values)``. In this case, the pub-like objects are converted to a list of circuits, and all the other fields (such as ``observables`` and ``parameter_values``) are ignored. Returns: The submitted job. c3B# UHn[U[5v M g7fN)r'r ).0ts r- #NoiseLearner.run..sC(Q:a00(sr,circuitsNtargetcalibration_idzSubmitting job using options %s) program_idinputsr,result_decoderr>backendinstance)r?r,r@rAr>)r?r,r@r>)allrcoercecircuitrr,_get_inputs_optionsr_get_runtime_optionsupdater&getattrrrr=r'r check_faultyloggerinfor$_run _program_idrget _instancer%r) r*r<pub coerced_pubsp options_dictlearner_optionsruntime_optionsr@r>tasks r-runNoiseLearner.runsT*C(CCC@HIL//4LI+78&>|&LM-BB<Ph' o& == 4==(D99,t}}B]B])4&$--2F2FGdmmZ88MM..t4 ! %T]]4DdKN 5G ::::??++-'/33D4D4D4FG- #  ==)-OI &0Z z5Z5Z.2mm.E.E + dmm%9 : :==%%++-'/33D4D4D4FG- & }}!!'')#) "  aJ8s KKcg)zReturn the program ID.z noise-learner)clss r-rONoiseLearner._program_idsr0cUR$)z6Return the backend the primitive query will be run on.)r&r3s r-rBNoiseLearner.backends }}r0cU(d[5Ulg[U[5(a[U5Ulg[U[5(a[ UR R5nURRUS'URUS'URUS'URUS'URUS'[S0UD6Ulg[S0UD6Ulg)zK Sets the options, ensuring that they are of type ``NoiseLearnerOptions``. twirling_strategymax_execution_time simulator environment experimentalNr\)rr2r'rrr resiliencelayer_noise_learningtwirlingstrategyrcrdrerf)r*r,ds r-r)NoiseLearner._set_optionss /1DM !4 5 5#G,DM !1 2 2w))>>?A%,%5%5%>%>A! "&-&@&@A" #$..AkN&22Am  ' 4 4An /4!4DM/:':DMr0c 0n/SQn[[5H,nURnX@;dMXB;dM[X5X'M. [ U5 [ U5 SUS'UR S05RSS5 U$)zReturns a dictionary of options that must be included in the program inputs, filtering out every option that is not part of the NoiseLearningOptions.)_VERSIONrcreTsupport_qiskitrfimageN)rrnamerrrrPpop)rUret ignored_namesfieldrqs r-rG NoiseLearner._get_inputs_optionss /0E::D#(A$\%78 1 !%# $  #''6 r0)r&r$r2r%)NN)r+z*Optional[Union[BackendV2, Session, Batch]]r,rsp"//77)&9C6()$8?G)&C9   8 $__r0