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IEEE Conference on Computer Communications (INFOCOM)

Qubit Allocation for Distributed Quantum Computing

Yingling Mao, Yu Liu, Yuanyuan Yang

Published May 17, 2023• 48 citations
Publisher
DOI
10.1109/infocom53939.2023.10228915

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Distribution of quantum circuits (DQC) is a promising strategy to enable large-scale quantum computations by utilizing a network of quantum computers. However, executing distributed quantum programs entails generating entanglements (to execute remote gates), which can incur significant latency and lead to qubits’ decoherence. Prior works on the problem of distributing quantum circuits with minimum circuit-execution time have only considered a static allocation of qubits to network nodes, presumably for simplicity. In this work, we show that distributing quantum circuits with a dynamic allocation of qubits, i.e., qubit allocation (over the quantum computers) that changes during the circuit execution, can lower circuit execution time. For this DQC problem wherein qubit allocation can be dynamic (i.e., change over time), we design efficient algorithms and demonstrate the effectiveness of designed techniques via extensive simulations over NetSquid, a quantum network simulator; our techniques outperform the best prior work (using static qubit allocation) by up to 50%.
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. In this paper, we propose a joint optimization framework for network topology design and qubit allocation in DQC to minimize the 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 further improve computational efficiency, we present a partially linearized version of the problem, making it solvable using any classical optimization solver. Extensive simulations on both random and real-world quantum circuits validate the effectiveness of our proposed approach, demonstrating its capability to handle complex quantum circuits while reducing communication costs in DQC.