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Qoala: an Application Execution Environment for Quantum Internet Nodes

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arxiv 2502.17296 v1 pith:OXESWYUM submitted 2025-02-24 quant-ph cs.NIcs.OS

classification quant-phcs.NIcs.OS
keywords qoalaprogramnetworkquantumexecutioncalledclassical-quantumframework
verification ladder T0 review T1 audit T2 compute T3 formal
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Recently, a first-of-its-kind operating system for programmable quantum network nodes was developed, called QNodeOS. Here, we present an extension of QNodeOS called Qoala, which introduces (1) a unified program format for hybrid interactive classical-quantum programs, providing a well-defined target for compilers, and (2) a runtime representation of a program that allows joint scheduling of the hybrid classical-quantum program, multitasking, and asynchronous program execution. Based on concrete design considerations, we put forward the architecture of Qoala, including the program structure and execution mechanism. We implement Qoala in the form of a modular and extendible simulator that is validated against real-world quantum network hardware (available online). However, Qoala is not meant to be purely a simulator, and implementation is planned on real hardware. We evaluate Qoala's effectiveness and performance sensitivity to latencies and network schedules using an extensive simulation study. Qoala provides a framework that opens the door for future computer science research into quantum network applications, including scheduling algorithms and compilation strategies that can now readily be explored using the framework and tools provided.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 2 citations worldwide. Full citation record

  1. Advanced Scheduling Strategies for Distributed Quantum Computing Jobs

    quant-ph 2026-02 conditional novelty 5.0 of 10

    Link-aware scheduling rules for distributed quantum computing jobs cut simulated batch makespan by about half versus FIFO/LIST, but the best-performing rule is a simple heuristic, not the trained RL agent.

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