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.

该研究论文介绍了一种使用深度强化学习的电路分区新方法，为量子计算和图分区的发展做出了贡献，开辟了解决这类问题的新范式。

深度强化学习下的多核量子架构电路划分

Circuit Partitioning for Multi-Core Quantum Architectures with Deep  Reinforcement Learning

This paper addresses quantum circuit mapping for Noisy Intermediate-Scale
Quantum (NISQ) computers. Since NISQ computers constraint two-qubit operations
on limited couplings, an input circuit must be transformed into an equivalent
output circuit obeying the constraints. The transformation often requires
additional gates that can affect the accuracy of running the circuit. Based
upon a previous work of quantum circuit mapping that leverages gate commutation
rules, this paper shows algorithms that utilize both transformation and
commutation rules. Experiments on a standard benchmark dataset confirm the
algorithms with more rules can find even better circuit mappings compared with
the previously-known best algorithms.

本文研究了针对噪声中等规模量子计算机的量子电路映射问题，并提出了一种算法来利用变换和交换规则来改进电路映射。实验结果表明，与先前已知的最佳算法相比，使用更多规则的算法可以找到更好的电路映射。