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原文摘要

Distributed variational quantum computing (DVQC) appears as the emerging paradigm to address the limitations of near-term quantum devices by decomposing large parameterized quantum circuits (PQCs) into smaller subcircuits executed across multiple quantum processors. In this context, how to incentivize quantum processors to participate actively is crucial for accelerating DVQC, yet remains largely unexplored in the literature. To address this significant gap, we propose a novel two-stage incentive framework for wireless DVQC. In the first stage, we minimize processor-side energy cost via a joint optimization of subcircuit parameters, number of noise measurements, and transmit power, efficiently solved using block coordinate descent (BCD) and successive convex approximation (SCA). In the second stage, we maximize aggregator-side system utility by selecting an optimal subset of processors based on their optimized energy costs derived from the first stage, formulating a non-convex combinatorial selection problem addressed using the alternating direction method of multipliers (ADMM). Extensive numerical experiments on the MNIST and CIFAR-10 datasets demonstrate that our DVQC framework achieves training and testing performance comparable to centralized quantum computing in terms of accuracy and loss, with similar convergence behavior on MNIST and slightly slower initial convergence on CIFAR-10. Moreover, our optimization approach consistently outperforms baseline schemes in energy efficiency and system utility, revealing a strong synergy between processor-level energy minimization and incentive-driven processor selection, where reduced energy costs directly enhances a processor's likelihood of selection and contributes to higher overall system utility.