Quantum processing units (QPUs) are currently exclusively available from cloud vendors. However, with recent advancements, hosting QPUs is soon possible everywhere. Existing work has yet to draw from research in edge computing to explore systems exploiting mobile QPUs, or how hybrid applications can benefit from distributed heterogeneous resources. Hence, this work presents an architecture for Quantum Computing in the edge-cloud continuum. We discuss the necessity, challenges, and solution approaches for extending existing work on classical edge computing to integrate QPUs. We describe how warm-starting allows defining workflows that exploit the hierarchical resources spread across the continuum. Then, we introduce a distributed inference engine with hybrid classical-quantum neural networks (QNNs) to aid system designers in accommodating applications with complex requirements that incur the highest degree of heterogeneity. We propose solutions focusing on classical layer partitioning and quantum circuit cutting to demonstrate the potential of utilizing classical and quantum computation across the continuum. To evaluate the importance and feasibility of our vision, we provide a proof of concept that exemplifies how extending a classical partition method to integrate quantum circuits can improve the solution quality. Specifically, we implement a split neural network with optional hybrid QNN predictors. Our results show that extending classical methods with QNNs is viable and promising for future work.

翻译：量子处理单元（QPU）目前仅由云供应商独家提供。然而，随着最新进展，QPU有望很快实现随处部署。现有研究尚未充分借鉴边缘计算领域的成果来探索利用移动QPU的系统，也未探讨混合应用程序如何受益于分布式异构资源。为此，本文提出了一种面向边缘-云连续体的量子计算架构。我们讨论了将现有经典边缘计算工作扩展以集成QPU的必要性、挑战及解决方案。阐述了如何通过热启动方法定义利用连续体中分层资源的工作流。随后，我们引入了一种搭载混合经典-量子神经网络（QNN）的分布式推理引擎，以帮助系统设计者适配具有最高异构性程度的复杂需求应用。我们提出了以经典层分割和量子线路切割为核心的解决方案，论证了跨连续体利用经典与量子计算的潜力。为评估该愿景的重要性与可行性，我们通过概念验证展示了如何扩展经典分割方法以集成量子线路，从而提升解决方案质量。具体而言，我们实现了一种可选配混合QNN预测器的分割神经网络。实验结果表明，将经典方法与QNN进行扩展具有可行性与前景，可为未来研究奠定基础。