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特性，在解决经典计算机难以处理的问题方面展现出巨大潜力。量子架构的可扩展性仍是一项重大挑战。多核量子架构被提出用以解决可扩展性问题，但同时也引发了硬件、通信及编译等方面的新挑战。其中一项挑战在于如何调整量子算法以适应量子计算机中不同核心的约束。本文提出了一种基于深度强化学习的电路划分创新方法，为量子计算与图划分领域的发展做出贡献。该工作是深度强化学习技术集成至量子电路映射中的首步探索，为此类问题的解决开辟了全新范式。