Resource-Efficient Distributed Quantum Learning over Wireless Networks with Qubit Reuse

Distributed quantum computing (DQC) mitigates the hardware limitations of near-term devices by partitioning large circuits into smaller segments executed on qubit-limited processors. However, even these reduced circuits often exceed the capacity of near-term devices that provide only extremely few reliable qubits. To address this, we introduce a qubit-reuse based DQC framework that exploits mid-circuit measurement and reset to enable scalable execution with minimal physical qubits. We consider distributed quantum learning (DQL) as a representative case study and formulate an energy minimization problem for the proposed framework that jointly optimizes quantum operation energy, measurement repetitions, and transmission power. To tackle the inherent non-convexity, we design a hybrid block coordinate descent (BCD) and successive convex approximation (SCA) algorithm for efficient solution. Extensive simulations on MNIST and CIFAR-10 demonstrate that the proposed qubit-reuse framework enables scalable execution on qubit-limited devices while achieving competitive performance compared to centralized quantum computing, and that the joint optimization algorithm significantly improves energy efficiency over baseline schemes.