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InterQ: Communication-Aware Scheduling Across Modular QPUs with Classical and Quantum Links

arXiv
Authors: Vinooth Kulkarni, Jaehyun Lee, Lauren Li, Aaron Orenstein, Xinpeng Li, Shuai Xu, Daniel Blankenberg, Vipin Chaudhary

Year

2026

Paper ID

63826

Status

Preprint

Abstract Read

~2 min

Abstract Words

201

Citations

0

Abstract

As quantum computing scales toward practical workloads, future systems are expected to move beyond single monolithic processors toward modular architectures that connect multiple QPUs. Different platforms realize this modularity through different communication models: superconducting systems rely on real-time classical links and dynamic-circuit coordination, trapped-ion systems use photonic interconnects for remote entanglement, and neutral-atom systems provide strong intra-core connectivity with proposed optical links for inter-core communication. This heterogeneity makes communication-aware scheduling essential for shared modular quantum cloud environments. We present InterQ, a communication-aware scheduler for modular QPU architectures with heterogeneous communication models. InterQ jointly considers qubit capacity, placement, parallel execution, and communication-driven dependencies across distributed subcircuits, while enabling adaptive circuit cutting to reduce makespan while balancing fidelity and communication overhead. The framework distinguishes classical-link execution, where measurement and feedforward impose synchronization constraints, from quantum-link execution, where entanglement distribution and state transfer determine coordination cost. Using a unified simulation framework to compare superconducting, trapped-ion, and neutral-atom modular systems, InterQ shows how communication models and scheduler-driven cutting decisions affect throughput, latency, and fidelity. Across evaluated workloads, InterQ exposes an architecture-dependent tradeoff: neutral-atom modular QPUs achieve the highest fidelity, superconducting systems minimize runtime, and trapped-ion systems provide a balanced intermediate profile across fidelity and makespan.

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  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2026 reference point for readers tracking recent quantum research.
  • As quantum computing scales toward practical workloads, future systems are expected to move beyond single monolithic processors toward modular architectures that connect...

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