  Quantum computing holds immense potential for solving classically intractable
problems by leveraging the unique properties of quantum mechanics. The
scalability of quantum architectures remains a significant challenge.
Multi-core quantum architectures are proposed to solve the scalability problem,
arising a new set of challenges in hardware, communications and compilation,
among others. One of these challenges is to adapt a quantum algorithm to fit
within the different cores of the quantum computer. This paper presents a novel
approach for circuit partitioning using Deep Reinforcement Learning,
contributing to the advancement of both quantum computing and graph
partitioning. This work is the first step in integrating Deep Reinforcement
Learning techniques into Quantum Circuit Mapping, opening the door to a new
paradigm of solutions to such problems.
