Quantum Approximate Optimization Algorithm for Bayesian network structure learning problem

Experimental code for the application of the QAOA algorithm for the Bayesian network structure learning problem. With this code, it is able to:

• Load data using de R script and compute de scores needed for the implementation
• Load the scores using Python into a data structure
• Initialize de QAOA with the needed hamiltonian to approach de problem
• Set parameters such as number of layers among others
• Execute QAOA
• Extract and analyze results
• Input a noise model to the simulation and analyze behaviour

Code is available for Qiskit library but also for myQLM. In this repo only the Qiskit code has been made available.