Learning-optimized qubit mapping and reuse to minimize intercore communication in modular quantum architectures
Phys. Rev. Applied 25, 054023 – Published 8 May, 2026
DOI: https://doi.org/10.1103/rfn7-c5n6
Abstract
Modular quantum architectures have emerged as a promising approach for scaling quantum computing systems by connecting multiple quantum processing units. However, this approach introduces significant challenges due to costly intercore operations between chips and quantum state transfers, which contribute to noise and quantum decoherence. This paper presents QARMA, a qubit-mapping approach using attention-based deep reinforcement learning for modular quantum architectures, along with its extension, QARMA-R, which incorporates dynamic qubit reuse. Our approach combines an attention-based mechanism with graph neural networks to learn optimal qubit allocation, routing, and reuse strategies that minimize intercore communication. Our experimental results show significant improvements over state-of-the-art approaches. Compared to highly optimized Qiskit with modular architecture configuration, QARMA-R reduces intercore communication by up to 100% (86% on average), while QARMA maintains 15%–40% improvement for larger circuits without reuse. Against traditional modular qubit mapping, our approach achieves a 97%–100% reduction in intercore operation. The proposed methods advance quantum circuit compilation and enable the execution of larger quantum algorithms on resource-constrained modular quantum systems, thereby contributing to the growing body of research on scalable quantum computing architectures.
