TemplateOptimization

class TemplateOptimization(*args, **kwargs)[source]

Class for the template optimization pass.

Parameters
  • template_list (list[QuantumCircuit()]) – list of the different template circuit to apply.

  • heuristics_backward_param (list[int]) – [length, survivor] Those are the parameters for applying heuristics on the backward part of the algorithm. This part of the algorithm creates a tree of matching scenario. This tree grows exponentially. The heuristics evaluates which scenarios have the longest match and keep only those. The length is the interval in the tree for cutting it and surviror is the number of scenarios that are kept. We advice to use l=3 and s=1 to have serious time advantage. We remind that the heuristics implies losing a part of the maximal matches. Check reference for more details.

  • heuristics_qubits_param (list[int]) – [length] The heuristics for the qubit choice make guesses from the dag dependency of the circuit in order to limit the number of qubit configurations to explore. The length is the number of successors or not predecessors that will be explored in the dag dependency of the circuit, each qubits of the nodes are added to the set of authorized qubits. We advice to use length=1. Check reference for more details.

Attributes

TemplateOptimization.is_analysis_pass

Check if the pass is an analysis pass.

TemplateOptimization.is_transformation_pass

Check if the pass is a transformation pass.

Methods

TemplateOptimization.name()

Return the name of the pass.

TemplateOptimization.run(dag)

param dag

DAG circuit.