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DUAL-BLADE: Dual-Path NVMe-Direct KV-Cache Offloading for Edge LLM Inference Progressive Semantic Communication for Efficient Edge-Cloud Vision-Language Models Efficient, VRAM-Constrained xLM Inference on Clients Folding Tensor and Sequence Parallelism for Memory-Efficient Transformer Training & Inference DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training AMMA: A Multi-Chiplet Memory-Centric Architecture for Low-Latency 1M Context Attention Serving RaMP: Runtime-Aware Megakernel Polymorphism for Mixture-of-Experts Spark Policy Toolkit: Semantic Contracts and Scalable Execution for Policy Learning in Spark Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directions PolyKV: A Shared Asymmetrically-Compressed KV Cache Pool for Multi-Agent LLM Inference A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizations ITAS: A Multi-Agent Architecture for LLM-Based Intelligent Tutoring Latency and Cost of Multi-Agent Intelligent Tutoring at Scale TACO: Efficient Communication Compression of Intermediate Tensors for Scalable Tensor-Parallel LLM Training FreeScale: Distributed Training for Sequence Recommendation Models with Minimal Scaling Cost CommFuse: Hiding Tail Latency via Communication Decomposition and Fusion for Distributed LLM Training A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning Usable Agent Discovery for Decentralized AI Systems Cloud to Edge: Benchmarking LLM Inference On Hardware-Accelerated Single-Board Computers Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioning Promoting Simple Agents: Ensemble Methods for Event-Log Prediction GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGA AGNT2: Autonomous Agent Economies on Interaction-Optimized Layer 2 Infrastructure FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels Stream-CQSA: Avoiding Out-of-Memory in Attention Computation via Flexible Workload Scheduling A Delta-Aware Orchestration Framework for Scalable Multi-Agent Edge Computing Federated Learning over Blockchain-Enabled Cloud Infrastructure Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not? Sherpa.ai Privacy-Preserving Multi-Party Entity Alignment without Intersection Disclosure for Noisy Identifiers
EFaaS: A Quantum-Classical Serverless Entangled Scheduler...
[Submitted on 26 May 2026 (v1), last revised 10 Aug 2026 (this v · 2026-05-27 · via cs.DC updates on arXiv.org

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Abstract:As quantum computing enters the Utility Era, realizing near-term advantage relies heavily on Hybrid Variational Quantum Algorithms (VQAs). These algorithms require a tightly coupled, iterative loop between a classical CPU optimizer and a Quantum Processing Unit (QPU). However, current quantum cloud access models are bottlenecked by decoupled batch-queues that sever this loop, introducing massive Time-to-Next-Shot (TTNS) latency. This delay inflates convergence time from minutes to hours and exposes the computation to quantum hardware drift, degrading algorithmic fidelity. Unlike prior work that relies on resource-wasting static hardware reservations or state-oblivious, stateless functions, we propose EFaaS, a novel serverless middleware designed specifically for hybrid quantum workflows. EFaaS fundamentally departs from existing architectures by treating classical parameter optimization and quantum circuit execution as entangled, session-aware events. Our main technical innovations are threefold: (1) a Calibration-Aware placement strategy that dynamically routes circuits to QPUs with warm calibration caches, circumventing cold-start penalties, (2) a Dual-Resource Fair Queuing scheduler that maximizes quantum utilization by strictly prioritizing active iterative loops, and (3) the "EF-QuantumFuture" programming abstraction, a novel primitive enabling classical speculative execution to mask compute latency. Across the evaluated baselines, EFaaS achieves TTNS reductions of 11.4%-94.3%, QDC gains of 2.02%-15.78% points, and convergence speedups of 83.2%-98.3%, while eliminating drift penalties.

Submission history

From: Nouhaila Innan [view email]
[v1] Tue, 26 May 2026 18:13:50 UTC (423 KB)
[v2] Mon, 10 Aug 2026 08:27:24 UTC (421 KB)