Architectural Vision for Quantum Computing in the Edge-Cloud Continuum

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.

arXiv comment16 pages, 5 figures, Vision Paper

References in corpus (27)

• Adam: A Method for Stochastic Optimization
• Knowledge Distillation: A Survey
• Variational Quantum Algorithms
• The information bottleneck method
• Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence
• The Variational Quantum Eigensolver: a review of methods and best practices
• An adaptive variational algorithm for exact molecular simulations on a quantum computer
• Challenges and Opportunities in Quantum Machine Learning
• Transfer learning in hybrid classical-quantum neural networks
• Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges
• Warm-starting quantum optimization
• Simulating Large Quantum Circuits on a Small Quantum Computer
• CompressAI: a PyTorch library and evaluation platform for end-to-end compression research
• The Bitter Truth About Quantum Algorithms in the NISQ Era
• Quantum autoencoders to denoise quantum data
• Constructing a virtual two-qubit gate by sampling single-qubit operations
• Reformulation of the No-Free-Lunch Theorem for Entangled Data Sets
• Simultaneous execution of quantum circuits on current and near-future NISQ systems
• On Warm-Starting Neural Network Training
• A Roadmap for Automating the Selection of Quantum Computers for Quantum Algorithms
• Evaluating Quantum Approximate Optimization Algorithm: A Case Study
• To quantum or not to quantum: towards algorithm selection in near-term quantum optimization
• Investigating the effect of circuit cutting in QAOA for the MaxCut problem on NISQ devices
• Predicting Good Quantum Circuit Compilation Options
• Warm-Starting and Quantum Computing: A Systematic Mapping Study
• A Serverless Cloud Integration For Quantum Computing
• torchdistill: A Modular, Configuration-Driven Framework for Knowledge Distillation

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