Modular, distributed and multi-core architectures are currently considered a promising approach for scalability of quantum computing systems. The integration of multiple Quantum Processing Units necessitates classical and quantum-coherent communication, introducing challenges related to noise and quantum decoherence in quantum state transfers between cores. Optimizing communication becomes imperative, and the compilation and mapping of quantum circuits onto physical qubits must minimize state transfers while adhering to architectural constraints. The compilation process, inherently an NP-hard problem, demands extensive search times even with a small number of qubits to be solved to optimality. To address this challenge efficiently, we advocate for the utilization of heuristic mappers that can rapidly generate solutions. In this work, we propose a novel approach employing Deep Reinforcement Learning (DRL) methods to learn these heuristics for a specific multi-core architecture. Our DRL agent incorporates a Transformer encoder and Graph Neural Networks. It encodes quantum circuits using self-attention mechanisms and produce outputs through an attention-based pointer mechanism that directly signifies the probability of matching logical qubits with physical cores. This enables the selection of optimal cores for logical qubits efficiently. Experimental evaluations show that the proposed method can outperform baseline approaches in terms of reducing inter-core communications and minimizing online time-to-solution. This research contributes to the advancement of scalable quantum computing systems by introducing a novel learning-based heuristic approach for efficient quantum circuit compilation and mapping.

翻译：模块化、分布式和多核架构目前被认为是实现量子计算系统可扩展性的一种有前景的途径。多个量子处理单元的集成需要经典和量子相干通信，这给核心间的量子态传输带来了与噪声和量子退相干相关的挑战。优化通信变得至关重要，因此量子电路到物理量子比特的编译与映射必须在遵守架构约束的同时，最小化态传输。编译过程本质上是一个NP难问题，即使要优化的量子比特数量很少，也需要大量的搜索时间才能求得最优解。为了有效应对这一挑战，我们主张利用能够快速生成解决方案的启发式映射器。在本工作中，我们提出了一种新颖的方法，采用深度强化学习方法为特定的多核架构学习这些启发式规则。我们的DRL智能体结合了Transformer编码器和图神经网络。它利用自注意力机制对量子电路进行编码，并通过一个基于注意力的指针机制产生输出，该机制直接表示逻辑量子比特与物理核心匹配的概率。这使得能够高效地为逻辑量子比特选择最优核心。实验评估表明，所提出的方法在减少核心间通信和最小化在线求解时间方面优于基线方法。本研究通过引入一种新颖的基于学习的启发式方法，用于高效的量子电路编译与映射，为可扩展量子计算系统的发展做出了贡献。