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This paper introduces EFaaS, a serverless middleware architecture designed to reduce Time-to-Next-Shot (TTNS) latency in hybrid variational quantum algorithms (VQAs). EFaaS achieves this by employing calibration-aware QPU placement, a dual-resource fair queuing scheduler prioritizing active iterative loops, and a "EF-QuantumFuture" programming abstraction for classical speculative execution. Results demonstrate TTNS reductions of up to 94.3% and convergence speedups up to 98.3% compared to existing cloud access models, while also mitigating hardware drift.
Hybrid quantum algorithms can be sped up by almost 2 orders of magnitude simply by co-scheduling classical and quantum resources in a stateful, calibration-aware manner.
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.
