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 works that rely 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.

翻译：随着量子计算进入效用时代，实现近期优势高度依赖于混合变分量子算法（VQAs）。此类算法需要在经典CPU优化器与量子处理单元（QPU）之间建立紧密耦合的迭代循环。然而，当前量子云访问模型受制于解耦批处理队列这一瓶颈，该队列切断了这一循环，引入了巨大的"下次执行间隔"（TTNS）延迟。这种延迟将收敛时间从分钟级膨胀至小时级，同时使计算暴露于量子硬件漂移风险，导致算法保真度下降。不同于以往依赖浪费资源的静态硬件预留或状态无关的无状态函数的方法，我们提出EFaaS——一种专为混合量子工作流设计的新型无服务器中间件。EFaaS在根本上区别于现有架构，它将经典参数优化与量子电路执行视为纠缠的、会话感知的事件。我们的主要技术创新包含三方面：（1）校准感知的放置策略，动态地将电路路由至具有热校准缓存的QPU，规避冷启动惩罚；（2）双资源公平排队调度器，通过严格优先处理活跃迭代循环来最大化量子利用率；（3）"EF-QuantumFuture"编程抽象——一种新型原语，支持经典推测执行以掩盖计算延迟。在评估的基线方法中，EFaaS实现了TTNS降低11.4%-94.3%、QDC提升2.02%-15.78个百分点、收敛速度提升83.2%-98.3%，同时消除了漂移惩罚。