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.

翻译：量子计算通过利用量子力学的独特性质，在解决经典难解问题上展现出巨大潜力。然而，量子架构的可扩展性仍然是一个重大挑战。多核量子架构被提出来解决可扩展性问题，但随之在硬件、通信和编译等方面带来了一系列新的挑战。其中一项挑战是如何调整量子算法以适应量子计算机的不同核心。本文提出了一种利用深度强化学习进行电路划分的新方法，为量子计算和图划分领域的进展做出了贡献。这项工作是首次将深度强化学习技术整合到量子电路映射中，为这类问题的解决开辟了新的范式。