Quantum Informed Recursive Optimization

An algorithm to facilitate the functionality of Quantum Informed Recursive Optimizations, as developed by J. Finzgar et. al. in Quantum-Informed 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 the problem object.

• The cost_operator, mixer, init_function and cl_cost_function in analogy to QAOAProblem instantiation.

Collection of mixers and auxiliary functions

qiro_RXMixer([solutions, exclusions])	RX-Mixer for QIRO algorithm.
qiro_init_function([solutions, exclusions])	State initialization function for QIRO algorithm.
find_max(single_cor, double_cor, res, solutions)	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