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 instanciation.
Collection of mixers and suplementary material#
Qrisp comes with a variety of predefined mixers to tackle various types of problem instances:

RXMixer for QIRO algorithm. 

State initiation function for QIRO algorithm. 

General 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