ó Óz iRãóv•SrSSKJr SSKJrJr SSKJrJr SSK r SSK J r "SS\ \5r "S S \5rg) z3Base classes for an approximate circuit definition.é)Ú annotations)ÚABCÚabstractmethod)ÚOptionalÚ SupportsFloatN)ÚQuantumCircuitcóf^•\rSrSrSrSSU4Sjjjr\\S Sj55r\S Sj5r Sr U=r $) ÚApproximateCircuitéz4A base class that represents an approximate circuit.có >•[TU]XS9 g)z` Args: num_qubits: number of qubit this circuit will span. name: a name of the circuit. )ÚnameN)ÚsuperÚ__init__)ÚselfÚ num_qubitsr Ú __class__s €Úb/home/james-whalen/.local/lib/python3.13/site-packages/qiskit/synthesis/unitary/aqc/approximate.pyrÚApproximateCircuit.__init__sø€ô ‰Ñ˜ÐÒ/ócó•[e)z‹ The property is not implemented and raises a ``NotImplementedException`` exception. Returns: a vector of parameters of this circuit. ©ÚNotImplementedError©rs rÚthetasÚApproximateCircuit.thetas ó €ô"Ð!rcó•[e)z¿ Constructs this circuit out of the parameters(thetas). Parameter values must be set before constructing the circuit. Args: thetas: a vector of parameters to be set in this circuit. r)rrs rÚbuildÚApproximateCircuit.build+rr©©N)rÚintr z Optional[str]ÚreturnÚNone©r#ú np.ndarray)rr&r#r$) Ú__name__Ú __module__Ú __qualname__Ú__firstlineno__Ú__doc__rÚpropertyrrrÚ__static_attributes__Ú __classcell__)rs@rr r s@ø†Ù>÷0ñ0ðØó"óóð"ðó"óö"rr có¤•\rSrSrSrS Sjr\S Sj5r\S Sj5r\ SSj5r \ RSSj5r \ \SSj55r S r g )ÚApproximatingObjectiveé7a A base class for an optimization problem definition. An implementing class must provide at least an implementation of the ``objective`` method. In such case only gradient free optimizers can be used. Both method, ``objective`` and ``gradient``, preferable to have in an implementation. có•SUlgr!©Ú_target_matrixrs rrÚApproximatingObjective.__init__>s €à15ˆÕrcó•[e)zÛ Computes a value of the objective function given a vector of parameter values. Args: param_values: a vector of parameter values for the optimization problem. Returns: a float value of the objective function. r©rÚ param_valuess rÚ objectiveÚ ApproximatingObjective.objectiveBó €ô"Ð!rcó•[e)zÓ Computes a gradient with respect to parameters given a vector of parameter values. Args: param_values: a vector of parameter values for the optimization problem. Returns: an array of gradient values. rr7s rÚgradientÚApproximatingObjective.gradientOr;rcó•UR$)z* Returns: a matrix being approximated r3rs rÚ target_matrixÚ$ApproximatingObjective.target_matrix\s€ð ×"Ñ"Ð"rcó•Xlg)zQ Args: target_matrix: a matrix to approximate in the optimization procedure. Nr3)rr@s rr@rAds €ð ,Õrcó•[e)zF Returns: the number of parameters in this optimization problem. rrs rÚ num_thetasÚ!ApproximatingObjective.num_thetasls €ô"Ð!rr3N)r#r$)r8r&r#r)r8r&r#r&r%)r@r&r#r$)r#r")r'r(r)r*r+rrr9r=r,r@ÚsetterrDr-r rrr0r07s~†ñô 6ðó "óð "ðó "óð "ðó#óð#ð×Ñó,óð,ðØó"óóó"rr0)r+Ú __future__rÚabcrrÚtypingrrÚnumpyÚnpÚqiskit.circuit.quantumcircuitrr r0r rrÚrMs4ðñ:Ý"ß#ß*Ûå8ô"˜¨ô"ôD="˜Sõ="r