i*SrSSKJr SSKJrJr SSKJr SSKJ r SSK J r SSK J r SSKJr SS KJrJr SS KJr SS KJr SS KJr S r"SS5rg!\a SrNf=f)zFA class to help understand the expected performance of estimator jobs.) annotations)OptionalSequence) QiskitError) PassManager)EstimatorPubLike) EstimatorPub) BackendV2) NeatPubResult NeatResult)ConvertISAToClifford) NoiseModel) EstimatorV2TFc\rSrSrSrSSSjjr\SSj5r\RSSj5rSSjr SSSjjr SSS jjr SSS jjr SS jr SS jrS rg)Neat%a9A class to help understand the expected performance of estimator jobs. The "Noisy Estimator Analyzer Tool" (or "NEAT") is a convenience tool that users of the :class:`~.Estimator` primitive can employ to analyze and predict the performance of their queries. Its simulate method uses ``qiskit-aer`` to simulate the estimation task classically efficiently, either in ideal conditions or in the presence of noise. The simulations' results can be compared with other simulation results or with primitive results results to draw custom figures of merit. .. code::python # Initialize a Neat object analyzer = Neat(backend) # Map arbitrary PUBs to Clifford PUBs cliff_pubs = analyzer.to_clifford(pubs) # Calculate the expectation values in the absence of noise r_ideal = analyzer.ideal_sim(cliff_pubs) # Calculate the expectation values in the presence of noise r_noisy = analyzer.noisy_sim(cliff_pubs) # Calculate the expectation values for a different noise model analyzer.noise_model = another_noise_model another_r_noisy = analyzer.noisy_sim(cliff_pubs) # Run the Clifford PUBs on a QPU r_qpu = estimator.run(cliff_pubs) # Calculate useful figures of merit using mathematical operators, for example the relative # difference between experimental and noisy results, ... rel_diff = abs(r_noisy[0] - r_qpu[0]) / r_noisy[0] # ... the signal-to-noise ratio between experimental and ideal results, ... ratio = r_qpu[0] / r_ideal[0] # ... or the absolute difference between results obtained with different noise models abs_diff = abs(r_noisy[0] - another_r_noisy[0]) Args: backend: A backend. noise_model: A noise model for the operations of the given backend. If ``None``, it defaults to the noise model generated by :meth:`NoiseModel.from_backend`. Nc[(d [S5eXlUbUUlg[R"USS9Ulg)NzpCannot initialize object of type 'Neat' since 'qiskit-aer' is not installed. Install 'qiskit-aer' and try again.F)thermal_relaxation)HAS_QISKIT_AER ValueError_backendr from_backend noise_model)selfbackendrs ]/home/james-whalen/.local/lib/python3.13/site-packages/qiskit_ibm_runtime/debug_tools/neat.py__init__ Neat.__init__TsO~6   &  ((UK cUR$)zG The noise model used by this analyzer tool for the noisy simulations.  _noise_modelrs rrNeat.noise_modelbs    rcXlg)z=Sets a new noise model. Args: value: A new noise model. Nr!)rvalues rrr$is "rcUR$)z) The backend used by this analyzer tool. )rr#s rr Neat.backendrs}}rcU(aURU5nO&UVs/sHn[R"U5PM nnSU(a UROSUS.n[ XS.S9n U R U5n U R 5n U V s/sH"n [U RR5PM$ nn [U5$s snf![a"n S[U 5;a [S5U eU eSn A ff=fs sn f)a Perform a noisy or noiseless simulation of the estimator task specified by ``pubs``. Args: pubs: The PUBs specifying the estimation task of interest. with_noise: Whether to perform an ideal, noiseless simulation (``False``) or a noisy simulation (``True``). cliffordize: Whether or not to automatically apply the :class:`.~ConvertISAToClifford` transpiler pass to the given ``pubs`` before performing the simulations. seed_simulator: A seed for the simulator. precision: The target precision for the estimates of each expectation value in the returned results. Returns: The results of the simulation. stabilizerN)methodrseed_simulator)backend_optionsdefault_precision)optionszinvalid parameterszCouldn't run the simulation, likely because the given PUBs contain one or more non-Clifford instructions. To fix, try setting ``cliffordize`` to ``True``.) to_cliffordr coercer AerEstimatorrunresultrstrrr dataevsr )rpubs with_noise cliffordizer, precision coerced_pubspr- estimatoraer_job aer_resulterrr pub_resultss r _simulateNeat._simulatexs2 ++D1L<@ADqL//2DLA#/94++t,  !(7X -- -  )J;EE*Q}QVVZZ0* E+&&1B #s3x/    I Fs# C6C )C4 C1C,,C1c*URUSX#U5$)a Perform an ideal, noiseless simulation of the estimator task specified by ``pubs``. This function uses ``qiskit-aer``'s ``Estimator`` class to simulate the estimation task classically. .. note:: To ensure scalability, every circuit in ``pubs`` is required to be a Clifford circuit, so that it can be simulated efficiently regardless of its size. For estimation tasks that involve non-Clifford circuits, the recommended workflow consists of mapping the non-Clifford circuits to the nearest Clifford circuits using the :class:`.~ConvertISAToClifford` transpiler pass, or equivalently, to use the Neat's :meth:`to_clifford` convenience method. Alternatively, setting ``cliffordize`` to ``True`` ensures that the :meth:`to_clifford` method is applied automatically to the given ``pubs`` prior to the simulation. Args: pubs: The PUBs specifying the estimation task of interest. cliffordize: Whether or not to automatically apply the :class:`.~ConvertISAToClifford` transpiler pass to the given ``pubs`` before performing the simulations. seed_simulator: A seed for the simulator. precision: The target precision for the estimates of each expectation value in the returned results. Returns: The results of the simulation. FrDrr8r:r,r;s r ideal_simNeat.ideal_simsF~~dE; RRrc*URUSX#U5$)a Perform a noisy simulation of the estimator task specified by ``pubs``. This function uses ``qiskit-aer``'s ``Estimator`` class to simulate the estimation task classically. .. note:: To ensure scalability, every circuit in ``pubs`` is required to be a Clifford circuit, so that it can be simulated efficiently regardless of its size. For estimation tasks that involve non-Clifford circuits, the recommended workflow consists of mapping the non-Clifford circuits to the nearest Clifford circuits using the :class:`.~ConvertISAToClifford` transpiler pass, or equivalently, to use the Neat's :meth:`to_clifford` convenience method. Alternatively, setting ``cliffordize`` to ``True`` ensures that the :meth:`to_clifford` method is applied automatically to the given ``pubs`` prior to the simulation. Args: pubs: The PUBs specifying the estimation task of interest. cliffordize: Whether or not to automatically apply the :class:`.~ConvertISAToClifford` transpiler pass to the given ``pubs`` before performing the simulations. seed_simulator: A seed for the simulator. precision: The target precision for the estimates of each expectation value in the returned results. Returns: The results of the simulation. TrGrHs r noisy_simNeat.noisy_simsF~~dD+yQQrc /nUHn[R"U5nUR[[[ 5/5R UR 5URURURS55 M U$)z Return the cliffordized version of the given ``pubs``. This convenience method runs the :class:`.~ConvertISAToClifford` transpiler pass on the PUBs' circuits. Args: pubs: The PUBs to turn into Clifford PUBs. Returns: The Clifford PUBs. F) r r1appendrr r3circuit observablesparameter_valuesr;)rr8r<pub coerced_pubs rr0Neat.to_cliffords C&--c2K   !5!7 89==k>Q>QR++00))  rc>SUR5RS3$)NzNeat(backend="z"))rnamer#s r__repr__ Neat.__repr__s  3 34B77r)rr"r)N)rBackendrzOptional[NoiseModel]returnNone)r[r)r&rr[r)r[rZ)r) r8Sequence[EstimatorPubLike]r9boolr:r^r, Optional[int]r;floatr[r )FNr) r8r]r:r^r,r_r;r`r[r )r8r]r[zlist[EstimatorPub])r[r5)__name__ __module__ __qualname____firstlineno____doc__rpropertyrsetterrrDrIrLr0rX__static_attributes__rrrr%s,\  !! "" 4'(4'4' 4' & 4'  4' 4'r"(, #S(#S#S& #S  #S  #SP"(, #R(#R#R& #R  #R  #RJ88rrN)re __future__rtypingrrqiskit.exceptionsrqiskit.transpiler.passmanagerrqiskit.primitives.containersr*qiskit.primitives.containers.estimator_pubr qiskit.providersr rZ+qiskit_ibm_runtime.debug_tools.neat_resultsr r 4qiskit_ibm_runtime.transpiler.passes.cliffordizationr qiskit_aer.noiser"qiskit_aer.primitives.estimator_v2rr2r ImportErrorrrirrrvsXM"%)59C1QU+NN p8p8 NsAA"!A"