Architectural Vision for Quantum Computing in the Edge-Cloud Continuum

• URL: http://arxiv.org/abs/2305.05238v1
• Date: Tue, 9 May 2023 08:00:10 GMT
• Title: Architectural Vision for Quantum Computing in the Edge-Cloud Continuum
• Abstract summary: We discuss the necessity, challenges, and solution approaches for extending existing work on classical edge computing to integrate QPUs. We introduce a distributed engine with hybrid classical-quantum neural networks (QNNs) to aid system designers in accommodating applications with complex requirements.
• Score: 2.4815104578619653
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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