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arXiv:2505.06162 · IEEE International Conference on Quantum Computing and Engineering (QCE)

Compilation Strategies for Quantum Network Programs Using Qoala

Samuel Oslovich, Bart van der Vecht, Stephanie Wehner

Published Aug 30, 2025• 2 citations
arXivHTMLPDF

Abstract

Execution of quantum network applications requires a software stack for nodes. Recently, the first designs and demonstrations have been proposed for such software stacks, including QNodeOS and its extension, Qoala. The latter enables compilation strategies previously not possible in QNodeOS. Here, we show how the extensions provided by Qoala can be used by a compiler to improve the performance of quantum network applications. We define new compilation strategies that allow the compiler to influence the scheduling and execution of quantum programs on a quantum network node. Through simulation, we demonstrate that our compilation strategies can reduce the execution time by up to 29.53% and increase the success probability by up to 25.12%. Our work highlights the potential of compiler optimizations for quantum network programs.

DOI
10.1109/qce65121.2025.00084

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Execution of quantum network applications requires a software stack for nodes. Recently, the first designs and demonstrations have been proposed for such software stacks, including QNodeOS and its extension, Qoala. The latter enables compilation strategies previously not possible in QNodeOS. Here, we show how the extensions provided by Qoala can be used by a compiler to improve the performance of quantum network applications. We define new compilation strategies that allow the compiler to influence the scheduling and execution of quantum programs on a quantum network node. Through simulation, we demonstrate that our compilation strategies can reduce the execution time by up to 29.53% and increase the success probability by up to 25.12%. Our work highlights the potential of compiler optimizations for quantum network programs.
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The goal of future quantum networks is to enable new internet applications that are impossible to achieve using solely classical communication. Up to now, demonstrations of quantum network applications and functionalities on quantum processors have been performed in ad-hoc software that was specific to the experimental setup, programmed to perform one single task (the application experiment) directly into low-level control devices using expertise in experimental physics. Here, we report on the design and implementation of the first architecture capable of executing quantum network applications on quantum processors in platform-independent high-level software. We demonstrate the architecture's capability to execute applications in high-level software, by implementing it as a quantum network operating system -- QNodeOS -- and executing test programs including a delegated computation from a client to a server on two quantum network nodes based on nitrogen-vacancy (NV) centers in diamond. We show how our architecture allows us to maximize the use of quantum network hardware, by multitasking different applications on a quantum network for the first time. Our architecture can be used to execute programs on any quantum processor platform corresponding to our system model, which we illustrate by demonstrating an additional driver for QNodeOS for a trapped-ion quantum network node based on a single 40Ca+^{40}\text{Ca}^+40Ca+ atom. Our architecture lays the groundwork for computer science research in the domain of quantum network programming, and paves the way for the development of software that can bring quantum network technology to society.
Abstract Quantum computers promise to transform our notions of computation by offering a completely new paradigm. To achieve scalable quantum computation, optimizing compilers and a corresponding software design flow will be essential. We present a software architecture for compiling quantum programs from a high-level language program to hardware-specific instructions. We describe the necessary layers of abstraction and their differences and similarities to classical layers of a computer-aided design flow. For each layer of the stack, we discuss the underlying methods for compilation and optimization. Our software methodology facilitates more rapid innovation among quantum algorithm designers, quantum hardware engineers, and experimentalists. It enables scalable compilation of complex quantum algorithms and can be targeted to any specific quantum hardware implementation.
The increasing quality and availability of Quantum Processing Units (QPUs) is fueling a growing interest in quan-tum computing across many technological areas. The resulting increase in demand for QPU resources necessitates Quantum Computing as a Service (QCaaS) providers to support a high throughput of quantum workloads. A major runtime bottleneck in current QCaaS software stacks is the computationally-intensive compilation step which requires significant compute. To address this, Oxford Quantum Circuits has introduced distributed compilation whereby quantum programs are com-piled in parallel and stored until the QPU is available. This has replaced our previous serial compilation approach where each program was compiled immediately prior to execution. From experiments using our production compilers and a simulated backend representing the QPU, we show that distributed com-pilation has resulted in a 78 % reduction in processing time as compared to serial compilation. This demonstrates that there are sizeable performance gains to program throughput attainable through the introduction of distributed compilation into a QCaaS architecture. We posit that the usefulness of this feature will only grow given the increasing complexity of quantum programs and the growing popularity of quantum-classical hybrid algorithms.
As quantum computing (QC) technologies mature, their integration into established high-performance computing (HPC) infrastructures is becoming a central objective for next-generation computing systems. However, unlocking the potential of hybrid platforms for computationally demanding workloads remains challenging. The mismatch between quantum and classical programming models, the limited maturity of quantum software stacks, and the scarcity of quantum processing units (QPUs) above all, necessitate scheduling strategies that go beyond standard HPC mechanisms to manage such heterogeneous and constrained resources. To address this issue, we investigate three distinct methodologies for HPC-QC resource scheduling: time-based multiplexing, dynamic resource management, and workflow decomposition. Experimental validation on production HPC clusters and real quantum hardware demonstrates the effectiveness of these approaches under different workload scenarios. Malleability and workflow strategies significantly optimize classical resource utilization, reducing consumption by up to 45.7% and 64% respectively, proving to be best fitted for hybrid jobs where quantum and classical workloads are evenly balanced. Conversely, time-multiplexing enhances QPU utilization and reduces execution time at the cluster level, making it the optimal strategy for the opposite context, which is characterized by high classical-quantum workload imbalances. These findings underscore the practical viability of tailored scheduling strategies for hybrid HPC-QC environments and highlight their complementarity in building efficient, scalable software stacks for next-generation quantum-accelerated facilities.