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.

利用深度强化学习的方法，以及模块化分布式体系结构，提出了一种高效的量子电路编译和映射的新型学习启发式方法，以解决量子计算系统中的通信最优化问题。

模块量子架构中基于注意力机制的深度强化学习用于量子比特分配

Attention-Based Deep Reinforcement Learning for Qubit Allocation in  Modular Quantum Architectures

We improve the quality of quantum circuits on superconducting quantum
computing systems, as measured by the quantum volume, with a combination of
dynamical decoupling, compiler optimizations, shorter two-qubit gates, and
excited state promoted readout. This result shows that the path to larger
quantum volume systems requires the simultaneous increase of coherence, control
gate fidelities, measurement fidelities, and smarter software which takes into
account hardware details, thereby demonstrating the need to continue to
co-design the software and hardware stack for the foreseeable future.

我们通过采用动力学解耦、编译器优化、更短的两量子比特门以及激发态激励读数的组合来提高超导量子计算系统的量子电路的质量（由量子体积测量），这表明实现更大量子体积系统的途径需要同时提高相干性、控制门保真度、测量保真度以及更智能的软件，这显示出需要继续共同设计可预见未来的软件和硬件堆栈的需求。