Handling Qubit Allocation and Topology Design at Scale for Distributed Quantum Computing

Abstract

Distributed Quantum Computing (DQC) enables the execution of quantum circuits across multiple interconnected quantum processing units (QPUs) but requiring efficient qubit allocation and network topology design to optimize computational performance. Proper qubit allocation minimizes entanglement costs across QPUs, balances computational workload, and ensures efficient execution of quantum computing tasks. Meanwhile, network topology plays a crucial role in reducing entanglement routing complexity and communication overhead for remote quantum gate operations. Therefore, in this paper, we propose a joint optimization framework for network topology design and qubit allocation in DQC to minimize communication overhead. We formulate the problem as a tractable integer nonlinear programming model that explicitly incorporates entanglement routing, thereby ensuring a more tractable optimization process. To improve computational efficiency, we present a partially linearized version of the problem, making it solvable using any classical optimization solver. To further address the scheduling of large-scale quantum circuits, we develop heuristics from edge contraction in the qubit interaction graph to pre-assign groups of qubits to QPUs, ensuring that our method remains effective at scale. Extensive simulations on real-world quantum circuits over various quantum clusters or distributed quantum networks validate the effectiveness of our proposed approach, demonstrating its capability to handle complex quantum circuits while reducing communication costs in DQC.