It is shown that the best-performing compilation strategy varies across the tested circuits and network configurations, and that both network topology and intra-QPU connectivity substantially affect the entanglement cost of execution.
Abstract
Distributed quantum computing (DQC) seeks to scale beyond the limits of monolithic processors by interconnecting multiple quantum processing units (QPUs) through entanglement-based links. Realizing this vision requires the co-design of hardware and software across the domains of quantum networking, compilation, and scheduling, yet existing tools remain fragmented between monolithic circuit compilers and long-distance quantum network simulators. We present an open-source, topology-informed framework for the compilation and scheduling of distributed quantum programs. Given an input circuit and a description of the target network, the framework partitions and reconstructs the circuit into a distributed program that respects the specified inter- and intra-QPU topology, covers cross-QPU operations through gate and state teleportation, and schedules the result under either deterministic or stochastic entanglement-generation models. By accepting and emitting standard OpenQASM, the framework interoperates with existing monolithic toolchains, and its standardized module interfaces allow partitioning and scheduling strategies to be interchanged and benchmarked. Using this framework, we show that the best-performing compilation strategy varies across the tested circuits and network configurations, and that both network topology and intra-QPU connectivity substantially affect the entanglement cost of execution. These findings underscore the need for a co-design approach to distributed quantum computing, which our framework is designed to support.
Distributed Quantum Computing (DQC) is essential for scaling quantum algorithms beyond monolithic constraints, yet current software ecosystems require tedious manual orchestration of low-level physical resources. This paper presents NetQMPI, a high-level Python framework that adapts the Message Passing Interface (MPI)...
F. J. Cardama, Jorge Vázquez-Pérez, Tomás F. Pena et al.· IEEE Access· 1 citation
The realization of practical quantum advantage requires executing large-scale circuits that far exceed the qubit capacity of any single quantum processor. To address this, two primary scaling strategies have emerged: circuit cutting, which utilizes classical resources to decompose circuits into smaller fragments, and m...
Ze-Fan Du, Wen-Rui Zhang, Jake Gesseck et al.· IEEE International Conferenc...· 0 citations
Distributing quantum states and entanglement between multiple pairs of nodes is a fundamental task in quantum communication and distributed quantum computing on large-scale quantum networks. In particular, the simultaneous distribution of quantum states or entanglement among multiple source-destination pairs (quant...
Shu-Ming Hu, Jun-Hao Wei, Nuo-Ya Yang et al.· Chinese Physics B· 0 citations
DPRQ is proposed, a qubit routing algorithm for minimizing inter-node communication in distributed quantum circuits divided into collective communication blocks that employs a dynamic programming-based technique focused on global circuit-level optimization, while capturing inter-block dependencies.
Dhaval Vaidya, Ruozhou Yu· Proceedings of the 3rd ACM S...· 0 citations
Distributed quantum computing (DQC) has been proposed as a way to scale quantum algorithms for practical applications beyond monolithic quantum processor architectures. Among these applications, quantum chemistry is widely regarded as one of the most promising use cases for quantum computing. In this work, we estimate...
G. Jones, Hassan Tariq Shafi, Zi-Xuan Wang et al.· 0 citations
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