Scalable Transpilation for Overcoming Restricted Connectivity in Distributed Superconducting Quantum Architectures
Abstract
Distributed Quantum Computing (DQC) offers a promising pathway to scale superconducting quantum hardware, but restricted inter- and intra-module connectivity creates a major transpilation bottleneck. Current DQC transpilation methods often assume idealized hardware connectivity, ignore subsequent intra-module routing, or suffer from substantial computational overheads that limit their applicability at scale. To address this, we introduce a scalable transpilation method tailored for the restricted connectivity of distributed superconducting quantum architectures. Specifically, our quantum interaction graph (QIG) approach couples a graph-partitioning mapping step with a fast SABRE-inspired routing heuristic. On a 48-qubit (3 QPUs × 16 qubits) architecture with all-to-all inter-core connectivity, our method reduces average aggregated costs by up to 24.72% (unstructured) and 24.01% (structured), while delivering >250× speed-ups for structured workloads compared to the state-of-the-art DMapS algorithm. On the 399-qubit (3×133) IBM Flamingo, where DMapS times out on dense circuits, our approach maintains practical runtimes. Against a DQC-adapted SABRE baseline, it reduces structured circuit routing costs by up to 51.94% (all-to-all inter-core topology) and 51.96% (restricted line topology). Ultimately, this work delivers a practical transpilation method for DQC at scale.
Related
- Taming Spacetime Overhead and Design Complexity in Distributed Fault-Tolerant Superconducting Quantum Computation
- All-to-All Connectivity in Superconducting Qubits via Metamaterial-Mediated Interactions
- Transpilation-Aware Runtime Prediction for Noisy Quantum Circuit Simulation
- Digital Annealer-Assisted CNOT Oriented Quantum Circuit Transpilation with Integrated QUBO Mapping and Routing
- Overcoming the Hong-Ou-Mandel interference limitation for the second photon from a two-photon cascade
BibTeX
@misc{azenha2026scalable,
title = {{Scalable Transpilation for Overcoming Restricted Connectivity in Distributed Superconducting Quantum Architectures}},
author = {Afonso Azenha and Ilia Polian and Sebastian Brandhofer},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.07175},
eprint = {2611.07175},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.07175},
}
RIS
TY - UNPB
TI - Scalable Transpilation for Overcoming Restricted Connectivity in Distributed Superconducting Quantum Architectures
AU - Afonso Azenha
AU - Ilia Polian
AU - Sebastian Brandhofer
PY - 2026
DA - 2026/11/04
PB - arXiv
DO - 10.48550/arxiv.2611.07175
UR - https://arxiv.org/abs/2611.07175
AB - Distributed Quantum Computing (DQC) offers a promising pathway to scale superconducting quantum hardware, but restricted inter- and intra-module connectivity creates a major transpilation bottleneck. Current DQC transpilation methods often assume idealized hardware connectivity, ignore subsequent intra-module routing, or suffer from substantial computational overheads that limit their applicability at scale. To address this, we introduce a scalable transpilation method tailored for the restricted connectivity of distributed superconducting quantum architectures. Specifically, our quantum interaction graph (QIG) approach couples a graph-partitioning mapping step with a fast SABRE-inspired routing heuristic. On a 48-qubit (3 QPUs × 16 qubits) architecture with all-to-all inter-core connectivity, our method reduces average aggregated costs by up to 24.72% (unstructured) and 24.01% (structured), while delivering >250× speed-ups for structured workloads compared to the state-of-the-art DMapS algorithm. On the 399-qubit (3×133) IBM Flamingo, where DMapS times out on dense circuits, our approach maintains practical runtimes. Against a DQC-adapted SABRE baseline, it reduces structured circuit routing costs by up to 51.94% (all-to-all inter-core topology) and 51.96% (restricted line topology). Ultimately, this work delivers a practical transpilation method for DQC at scale.
ER -