Quantum annealing can be used to solve optimization prob-
lems. Quantum processors, performing quantum annealing, operate mini-
mizing a cost function. The central issue is to map the cost function which
has p-body interactions into a function with at most 2-body interactions.
In the already existing method of minor embedding, xing the number of
ancillae qubits for highly interacting models becomes impractical. Here
we propose a technique for obtaining approximate mapping based on ge-
netic algorithms. We verify the feasibility of this procedure by mapping
ferromagnetic p-spin model in two analytically solvable cases.
Based on the manuscript submitted to the journal of Quantum Machine Intelligence-
Passarelli, G. et. al, An evolutionary strategy for nding eective quantum 2-body
Hamiltonians of p-body interacting systems.