Quantum Informed Recursive Optimization#
An algorithm to facilitate the functionality of Quantum Informed Recursive Optimizations, as developed by J. Finzgar et. al. in QuantumInformed Recursive Optimization Algorithms (2023) .
It is based on updating the problem instance based on correlations, that are in turn established with a QAOA protocol. For further info have a look at our tutorial on QIRO!
The central data structure of the QIRO module is the QIROProblem class.
QIROProblem#
The QIROProblem encapsulates the required prerequesites to run the algorithm:
The
problem
to be solved, which is not necessarly a graph.The
replacement_routine
, which has the job of performing the aforementioned specific reductions to theproblem
object.The
cost_operator
,mixer
,init_function
andcl_cost_function
in analogy to QAOAProblem instantiation.
Collection of mixers and auxiliary functions#

RXMixer for QIRO algorithm. 

State initialization function for QIRO algorithm. 

Subroutine for finding the values with maximal correlation in the QIRO algorithm. 
QIRO implementations of problem instances#
For implemented problem instances see the QIRO implementations page