EFaaS: A Quantum-Classical Serverless Entangled Scheduler for Hybrid Variational Algorithms

2026-05-26

AI Summary

Current hybrid variational quantum algorithms suffer from high classical–quantum iteration latency, slow convergence, and susceptibility to hardware drift due to the decoupled batch-processing architecture of quantum cloud platforms. This work proposes a session-aware serverless middleware tailored for hybrid quantum workflows, enabling tight coordination between classical optimization and quantum execution through calibration-aware QPU placement, dual-resource fair queue scheduling, and a novel EF-QuantumFuture programming primitive that supports classical speculative execution. Experimental results demonstrate that the proposed approach reduces TTNS latency by 11.4%–94.3%, improves device utilization by 2.02–15.78 percentage points, accelerates convergence by 83.2%–98.3%, and effectively mitigates performance degradation caused by hardware drift.

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 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.

Problem

• Hybrid Variational Quantum Algorithms
• Time-to-Next-Shot latency
• quantum hardware drift
• quantum-classical coupling
• serverless quantum computing

Innovation

• serverless quantum computing
• hybrid variational algorithms
• entangled scheduling
• calibration-aware placement
• speculative execution