Quantum

Scalable Transpilation for Overcoming Restricted Connectivity in Distributed Superconducting Quantum Architectures

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 06/11/2026, 05:13:25 UTC.

Briefings

A new scalable transpilation method for distributed superconducting quantum architectures uses a quantum interaction graph (QIG) approach combining graph-partitioning mapping with a fast SABRE-inspired routing heuristic. On a 48-qubit 3-QPU architecture, the method reduces average aggregated costs by up to 24.72% (unstructured) and 24.01% (structured), while delivering over 250x speed-ups for structured workloads compared to the state-of-the-art DMapS algorithm. On the 399-qubit IBM Flamingo, the approach maintains practical runtimes where DMapS times out on dense circuits.

Key points

• The method uses a quantum interaction graph (QIG) approach that couples a graph-partitioning mapping step with a fast SABRE-inspired routing heuristic.
• On a 48-qubit 3-QPU architecture, the method reduces average aggregated costs by up to 24.72% (unstructured) and 24.01% (structured) compared to DMapS.

Context

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 methods often assume idealized hardware connectivity or suffer from substantial computational overheads that limit their applicability at scale.

Limitations

The evidence is from an arXiv preprint (arXiv:2611.07175v1) and does not include peer-reviewed validation or experimental results from a live system. The performance gains are theoretical estimates based on simulation and benchmarking, and the specific hardware configurations tested (48-qubit and 399-qubit IBM Flamingo) are limited to these specific architectures.

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