ó Óz i• ãóB•SrSSKJr SSKJr SSKJr "SS\5rg)z3Combine consecutive Cliffords over the same qubits.é)ÚTransformationPass)Ú control_flow)ÚCliffordcó<•\rSrSrSr\R S5rSrg)ÚOptimizeCliffordsézšCombine consecutive Cliffords over the same qubits. This serves as an example of extra capabilities enabled by storing Cliffords natively on the circuit. cóî•/nSn/nUR5Hšn[UR[5(a\UcUR U5 U/nO@UR UR :XaUR U5 OUR U5 U/nUnM~U(aUR U5 Sn/nMœ U(aUR U5 UHœn[ U5S::aM[USR 5VVs0sHupxX‡_M n nnUSRn [U5H-upµU S:”dM [R"URU SS9n M/ URXjU SS9 Mž U$s snnf)zŒRun the OptimizeCliffords pass on `dag`. Args: dag (DAGCircuit): the DAG to be optimized. Returns: DAGCircuit: the optimized DAG. NérT)ÚfrontF)Ú cycle_check) Útopological_op_nodesÚ isinstanceÚoprÚappendÚqargsÚlenÚ enumerateÚcomposeÚreplace_block_with_op) ÚselfÚdagÚblocksÚ prev_nodeÚ cur_blockÚnodeÚ cur_nodesÚixÚqbÚ wire_pos_mapÚcliffÚis Úr/home/james-whalen/.local/lib/python3.13/site-packages/qiskit/transpiler/passes/optimization/optimize_cliffords.pyÚrunÚOptimizeCliffords.runs\€ðˆØˆ ؈ ð×,Ñ,Ö.ˆDܘ$Ÿ'™'¤8×,Ñ,ØÑ$Ø—M‘M )Ô,Ø!% ‘Ià —‘¨$¯*©*Ó4Ø!×(Ñ(¨Õ.àŸ ™  iÔ0Ø%) F˜ à ’ öØ—M‘M )Ô,Ø Ø’ ñ'/ö* Ø M‰M˜)Ô $ó ˆIä9‹~ Ó"Ùä1:¸9ÀQ¹<×;MÑ;MÔ1NÔOÒ1N¡v r˜BšFÑ1NˆLÑOð˜a‘L—O‘OˆEÜ$ YÖ/‘Øq•5Ü$×,Ò,¨T¯W©W°eÀ4ÑH’Eñ0ð × %Ñ % i¸ ÐRWÐ %Ó Xñ ð ˆ ùóPsÃ>E1©N) Ú__name__Ú __module__Ú __qualname__Ú__firstlineno__Ú__doc__rÚtrivial_recurser#Ú__static_attributes__r%ór"rrs!†ñð ×!Ñ!ñ>ó"ó>r-rN)r*Úqiskit.transpiler.basepassesrÚqiskit.transpiler.passes.utilsrÚqiskit.quantum_info.operatorsrrr%r-r"Úr1s"ðñ:å;Ý7Ý2ôEÐ*õEr-